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JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER

JAVIER CABRERA-ARTEAGA

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JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER

JAVIER CABRERA-ARTEAGA

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for

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For WebAssembly.

for WebAssembly

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for WebAssembly

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Software is a tool that allows you to download and install software.

Software

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Software is a tool that allows you to download and install software.

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Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect

al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col

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Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect

al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col

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Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect

al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col

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Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect

al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col

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Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect

al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col

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2024

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Stockholm, Sweden, March 2024.

Stockholm, Sweden, March 2024

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2024

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Javier Cabrera-Arteaga, March 7th 2024.

© Javier Cabrera-Arteaga , March 7th 2024

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Javier Cabrera-Arteaga, March 7th 2024.

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Javier Cabrera-Arteaga, March 7th 2024.

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Javier Cabrera-Arteaga, March 7th 2024.

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Javier

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Javier Cabrera-Arteaga, March 7th 2024.

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Javier Cabrera-Arteaga, March 7th 2024.

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978-91-8040-822-6

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TRITA-EECS-AVL-2024:10 ISBN 978-91-8040-822-6.

TRITA-EECS-AVL-2024:10 ISBN 978-91-8040-822-6

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Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract

Abstract

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Diversification

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This work aims to harden the security within the WebAssembly ecosystem through the introduction of software diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.

This work aims to harden the security within the WebAssembly ecosystem through the introduction of Software Diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.

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This work aims to harden the security within the WebAssembly ecosystem through the introduction of software diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.

This work aims to harden the security within the WebAssembly ecosystem through the introduction of Software Diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.

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tools)

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This work introduces three tools: CROW, MEWE as compiler-based approaches, and WASM-MUTATE as a binary-based approach. Each tool has been specifically designed to tackle a unique facet of Software Diversification. We present empirical evidence demonstrating the potential application of our Software Diversification methods to WebAssembly programs in two distinct ways: Offensive and Defensive Software Diversification. Our research into Offensive Software Diversification in WebAssembly unveils potential paths for enhancing the detection of WebAssembly malware. On the other hand, our experiments in Defensive Software Diversification show that WebAssembly programs can be hardened against side-channel attacks, specifically the Spectre attack.

This work introduces three tools) CROW, MEWE as compiler-based approaches, and WASM-MUTATE as a binary-based approach. Each tool has been specifically designed to tackle a unique facet of Software Diversification. We present empirical evidence demonstrating the potential application of our Software Diversification methods to WebAssembly programs in two distinct ways: Offensive and Defensive Software Diversification. Our research into Offensive Software Diversification in WebAssembly unveils potential paths for enhancing the detection of WebAssembly malware. On the other hand, our experiments in Defensive Software Diversification show that WebAssembly programs can be hardened against side-channel attacks, specifically the Spectre attack.

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This work introduces three tools) CROW, MEWE as compiler-based approaches, and WASM-MUTATE as a binary-based approach. Each tool has been specifically designed to tackle a unique facet of Software Diversification. We present empirical evidence demonstrating the potential application of our Software Diversification methods to WebAssembly programs in two distinct ways: Offensive and Defensive Software Diversification. Our research into Offensive Software Diversification in WebAssembly unveils potential paths for enhancing the detection of WebAssembly malware. On the other hand, our experiments in Defensive Software Diversification show that WebAssembly programs can be hardened against side-channel attacks, specifically the Spectre attack.

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Side-Channels

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Keywords: WebAssembly, Software Diversification, Side-Channels.

Keywords: WebAssembly, Software Diversification, Side-Channels

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som

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Denna avhandling syftar till att starka sikerheten inom WebAssembly-ekosystemet genom inforandet av metoder och verktyg for mjukvarudiversifiering. Mjukvarudiversifiering ar en strategi som ar utformad for att dka kostnaderna for att exploatera sarbarheter genom att gora programvaran of6érutsagbar. Fdérutsagbarheten inom ekosystem kan minskas genom att automatiskt generera olika programvaruvarianter. Dessa varianter forstarker observerbara egenskaper so vanligtvis anvands for att starta attacker och kan i manga fall helt eliminera sadana sarbarheter.

Denna avhandling syftar till att starka sikerheten inom WebAssembly-ekosystemet genom inforandet av metoder och verktyg for mjukvarudiversifiering. Mjukvarudiversifiering ar en strategi som ar utformad for att dka kostnaderna for att exploatera sarbarheter genom att gora programvaran of6érutsagbar. Fdérutsagbarheten inom ekosystem kan minskas genom att automatiskt generera olika programvaruvarianter. Dessa varianter forstarker observerbara egenskaper som vanligtvis anvands for att starta attacker och kan i manga fall helt eliminera sadana sarbarheter.

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Webbsideresurser,

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Websideresurser, inklusive de som innehaller exekverbar WebAssembly, skickas i en modern digital kontext huvudsakligen fran centraliserade datacenter. Tusentals edge-enheter, i samarbete med webblasarklienter, kor miljontals identiska WebAssembly-instantieringar varje sekund. Detta fenomen skapar ett hdogst forutsagbart ekosystem, dar potentiella angripare kan forutse beteenden antingen i webblasare eller backend-noder. En sadan forutsagbarhet dkar potentialen for sarbarheter inom dessa ekosystem och Oppnar doérren f6r sidkanal-och minnesattacker med stor paverkan. Till exempel kan en brist i en webblasare, framkallad av ett defekt WebAssembly-program, ha potential att paverka miljontals anvandare.

Webbsideresurser, inklusive de som innehaller exekverbar WebAssembly, skickas i en modern digital kontext huvudsakligen fran centraliserade datacenter. Tusentals edge-enheter, i samarbete med webblasarklienter, kor miljontals identiska WebAssembly-instantieringar varje sekund. Detta fenomen skapar ett hdogst forutsagbart ekosystem, dar potentiella angripare kan forutse beteenden antingen i webblasare eller backend-noder. En sadan forutsagbarhet dkar potentialen for sarbarheter inom dessa ekosystem och Oppnar doérren f6r sidkanal-och minnesattacker med stor paverkan. Till exempel kan en brist i en webblasare, framkallad av ett defekt WebAssembly-program, ha potential att paverka miljontals anvandare.

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av

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WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

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har

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WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

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Dessutom

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WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

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officiellt

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WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.

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  • officiallt

defensiv

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Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensive och defensive mjukvarudiversifiering. Var forskning om offensive mjukvarudiversifiering i WebAssembly avsléjar potentialla vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensive mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.

Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensiv och defensiv mjukvarudiversifiering. Var forskning om offensiv mjukvarudiversifiering i WebAssembly avsléjar potentiella vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensiv mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.

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  • defensive

potentiella

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Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensive och defensive mjukvarudiversifiering. Var forskning om offensive mjukvarudiversifiering i WebAssembly avsléjar potentialla vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensive mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.

Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensiv och defensiv mjukvarudiversifiering. Var forskning om offensiv mjukvarudiversifiering i WebAssembly avsléjar potentiella vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensiv mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.

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  • potentialla

offensiv

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Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensive och defensive mjukvarudiversifiering. Var forskning om offensive mjukvarudiversifiering i WebAssembly avsléjar potentialla vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensive mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.

Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensiv och defensiv mjukvarudiversifiering. Var forskning om offensiv mjukvarudiversifiering i WebAssembly avsléjar potentiella vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensiv mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.

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  • offensive

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

2021.

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

https://doi.org/10.14722/madweb.

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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2021),

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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Web

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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the

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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Defenses

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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on

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

Workshop

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

Martin

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

Baudry,

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 23004

Benoit

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 2021.

Vera-Pérez,

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • https://doi.org/10.14722/madweb.

Oscar

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • pages

Floros,

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 12

Orestis

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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  • 2021),

Cabrera-Arteaga,

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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Javier

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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insert '' by 'Martin'

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4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Martin

insert '' by 'Baudry,'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Baudry,

insert '' by 'Benoit'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Benoit

insert '' by 'Vera-Pérez,'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Vera-Pérez,

insert '' by 'Oscar'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Oscar

insert '' by 'Floros,'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Floros,

insert '' by 'Orestis'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Orestis

insert '' by 'Cabrera-Arteaga,'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Cabrera-Arteaga,

insert '' by 'Javier'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Javier

insert '' by 'WebAssembly'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • WebAssembly

Diversification

replace 'Diversification' by 'for'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • for

Code

replace 'Code' by 'Diversification'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Diversification

CROW:

replace 'CROW:' by 'Code'

Happy suggestion

4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Code

insert '' by '3564007)'

Happy suggestion

3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)

3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007

Suggestions

  • 3564007)

insert '' by '-'

Happy suggestion

3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)

3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007

Suggestions

  • -

3564007

replace '3564007' by '3564007)'

Happy suggestion

3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)

3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007

Suggestions

  • 3564007)

00324

delete '00324' by ''

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

https://www.sciencedirect.com/science/article/pii/S01674048240

replace 'https://www.sciencedirect.com/science/article/pii/S01674048240' by '00324.'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • 00324.

pages

replace 'pages' by 'https://www.sciencedirect.com/science/article/pii/S01674048240'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • https://www.sciencedirect.com/science/article/pii/S01674048240

20

replace '20' by 'pages'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • pages

2024,

replace '2024,' by '20'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • 20

Security,

replace 'Security,' by '2024,'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • 2024,

&

replace '&' by 'Security,'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Security,

Computers

replace 'Computers' by '&'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • &

Benoit

replace 'Benoit' by 'Baudry'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Baudry

Monperrus,

replace 'Monperrus,' by 'Benoit'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Benoit

Martin

replace 'Martin' by 'Monperrus,'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Monperrus,

Fitzgerald,

replace 'Fitzgerald,' by 'Martin'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Martin

Nicholas

replace 'Nicholas' by 'Fitzgerald,'

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2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Fitzgerald,

Cabrera-Arteaga,

replace 'Cabrera-Arteaga,' by 'Nicholas'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

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  • Nicholas

Javier

replace 'Javier' by 'Cabrera-Arteaga,'

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2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Cabrera-Arteaga,

for

replace 'for' by 'WebAssembly'

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2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • WebAssembly

Diversification

replace 'Diversification' by 'for'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • for

Binary

replace 'Binary' by 'Diversification'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Diversification

Effective

replace 'Effective' by 'Binary'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Binary

and

replace 'and' by 'Effective'

Happy suggestion

2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • Effective

Fast

replace 'Fast' by 'and'

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2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

Suggestions

  • and

WASM-MUTATE:

replace 'WASM-MUTATE:' by 'Fast'

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2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

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  • Fast

02067

replace '02067' by '02067.'

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1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067.

1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067

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  • 02067.

insert '' by 'Tim'

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1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067.

1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067

Suggestions

  • Tim

Cabrera-Arteaga,Tim

replace 'Cabrera-Arteaga,Tim' by 'Cabrera-Arteaga,'

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1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067.

1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067

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  • Cabrera-Arteaga,

,Tim

Put a space after the comma.

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1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067

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  • , Tim

PAPERS

replace 'PAPERS' by 'PAPERS.'

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LIST OF PAPERS.

LIST OF PAPERS

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  • PAPERS.

https://doi.org/10.1145/3397537.3397567

replace 'https://doi.org/10.1145/3397537.3397567' by 'https://doi.org/10.1145/3397537.3397567.'

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5. Superoptimization of WebAssembly Bytecode Javier Cabrera-Arteaga, Shrinish Donde, Jian Gu, Orestis Floros, Lucas Satabin, Benoit Baudry, Martin Monperrus Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming (Programming 2021), MoreVMs, 4 pages https://doi.org/10.1145/3397537.3397567.

5. Superoptimization of WebAssembly Bytecode Javier Cabrera-Arteaga, Shrinish Donde, Jian Gu, Orestis Floros, Lucas Satabin, Benoit Baudry, Martin Monperrus Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming (Programming 2021), MoreVMs, 4 pages https://doi.org/10.1145/3397537.3397567

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  • https://doi.org/10.1145/3397537.3397567.

insert '' by 'all'

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I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen - all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.

I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen -all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.

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  • all

-all

replace '-all' by '-'

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I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen - all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.

I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen -all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.

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  • -

insert '' by 'an'

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I want to thank the distinguished members of my jury. Professor Sukyoung Ryu, thanks for accepting to take the role of an opponent in my defense. Professor Dilian Gurov, thanks for taking the advance review of this work. Professors, Quentin Stiévenart, Bjorn De Sutter, and Weihang Wang, thank you all for being part of my grading committee and for your invaluable insights.

I want to thank the distinguished members of my jury. Professor Sukyoung Ryu, thanks for accepting to take the role of opponent in my defense. Professor Dilian Gurov, thanks for taking the advance review of this work. Professors, Quentin Stiévenart, Bjorn De Sutter, and Weihang Wang, thank you all for being part of my grading committee and for your invaluable insights.

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  • an

immeasurably

delete 'immeasurably' by ''

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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contributed

replace 'contributed' by 'immensely'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • immensely

has

replace 'has' by 'contributed'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • contributed

difficult,

replace 'difficult,' by 'has'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • has

how

replace 'how' by 'difficult,'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • difficult,

matter

replace 'matter' by 'how'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • how

no

replace 'no' by 'matter'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • matter

step,

replace 'step,' by 'no'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • no

Each

replace 'Each' by 'step,'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • step,

challenges.

replace 'challenges.' by 'Each'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Each

by

replace 'by' by 'challenges.'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • challenges.

marked

replace 'marked' by 'by'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • by

journey

replace 'journey' by 'marked'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • marked

a

replace 'a' by 'journey'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • journey

see

replace 'see' by 'a'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • a

I

replace 'I' by 'see'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • see

years,

replace 'years,' by 'I'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • I

five

replace 'five' by 'years,'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • years,

past

replace 'past' by 'five'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • five

these

replace 'these' by 'past'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • past

over

replace 'over' by 'these'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • these

back

replace 'back' by 'over'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • over

look

replace 'look' by 'back'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • back

I

replace 'I' by 'look'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • look

as

replace 'as' by 'I'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • I

Now,

replace 'Now,' by 'as'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • as

KTH.

replace 'KTH.' by 'Now,'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Now,

at

replace 'at' by 'KTH.'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • KTH.

Engineering

replace 'Engineering' by 'at'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • at

Software

replace 'Software' by 'Engineering'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Engineering

in

replace 'in' by 'Software'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Software

position

replace 'position' by 'in'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • in

Ph.D.

replace 'Ph.D.' by 'Position'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Position

a

replace 'a' by 'Ph.D.'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Ph.D.

opportunity:

replace 'opportunity:' by 'a'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • a

life-changing

replace 'life-changing' by 'opportunity:'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • opportunity:

a

replace 'a' by 'life-changing'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • life-changing

presented

replace 'presented' by 'a'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • a

Vera,

replace 'Vera,' by 'presented'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • presented

Luis

replace 'Luis' by 'Vera,'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Vera,

Oscar

replace 'Oscar' by 'Luis'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Luis

friend,

replace 'friend,' by 'Oscar'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Oscar

and

replace 'and' by 'friend,'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • friend,

mentor

replace 'mentor' by 'and'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • and

my

replace 'my' by 'mentor'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • mentor

uncertainty,

replace 'uncertainty,' by 'my'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • my

of

replace 'of' by 'uncertainty,'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • uncertainty,

phase

replace 'phase' by 'of'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • of

this

replace 'this' by 'phase'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • phase

During

replace 'During' by 'this'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • this

pursuits.

replace 'pursuits.' by 'During'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

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  • During

academic

replace 'academic' by 'pursuits.'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • pursuits.

my

replace 'my' by 'academic'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • academic

advance

replace 'advance' by 'my'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • my

to

replace 'to' by 'advance'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • advance

whether

replace 'whether' by 'to'

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Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • to

of

replace 'of' by 'whether'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • whether

journey

replace 'journey' by 'Journey'

Happy suggestion

Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.

Suggestions

  • Journey

-this

replace '-this' by 'this'

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First and foremost, to my beloved wife, Ilena, this journey has been yours as mine.

First and foremost, to my beloved wife, Ilena -this journey has been yours as mine.

Suggestions

  • this

Ilena

replace 'Ilena' by 'Ilena,'

Happy suggestion

First and foremost, to my beloved wife, Ilena, this journey has been yours as mine.

First and foremost, to my beloved wife, Ilena -this journey has been yours as mine.

Suggestions

  • Ilena,

insert '' by '(SSF)'

Happy suggestion

These last five years have been supported by the Stiftelsen for Strategisk Forskning (SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.

These last five years have been supported by the Stiftelsen for Strategisk Forskning(SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.

Suggestions

  • (SSF)

Forskning(SSF)

replace 'Forskning(SSF)' by 'Forskning'

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These last five years have been supported by the Stiftelsen for Strategisk Forskning (SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.

These last five years have been supported by the Stiftelsen for Strategisk Forskning(SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.

Suggestions

  • Forskning

mi

Did you mean “me” or “my”?

Happy suggestion

Last but never least, to my family in Cuba, especially to “mi abuela Mary”. Family values led me to this successful journey.

Suggestions

  • me
  • my

outline

replace 'outline' by 'outline:'

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Introduction 1.1 1.2 1.3 1.4 1.5 1.6 WebAssembly Predictability in WebAssembly ecosystems Problem statements Approach: Software Diversification Summary of research papers Thesis outline:

Introduction 1.1 1.2 1.3 1.4 1.5 1.6 WebAssembly Predictability in WebAssembly ecosystems Problem statements Approach: Software Diversification Summary of research papers Thesis outline

Suggestions

  • outline:

Thesis

replace 'Thesis' by 'thesis.'

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I thesis.

I Thesis

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  • thesis.

insert '' by 'Contents:'

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Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents

Contents

Suggestions

  • Contents:

Papers

replace 'Papers' by 'Papers.'

Happy suggestion

List of Papers.

List of Papers

Suggestions

  • Papers.

Diversification

replace 'Diversification' by 'Divers'

Happy suggestion

Background and state of the art 2.1 WebAssembly 2.1.1 From source code to WebAssembly 2.1.2 WebAssembly’s binary format 2.1.3 WebAssembly’s runtime 2.1.4 WebAssembly’s control-flow 2.1.5 Security and reliability for WebAssembly 2.1.6 Open challenges 2.2 Software diversification 2.2.1 Automatic generation of software variants 2.2.2 Equivalence Checking 2.2.3 Variants deployment 2.2.4 Measuring Software Divers

Background and state of the art 2.1 WebAssembly 2.1.1 From source code to WebAssembly 2.1.2 WebAssembly’s binary format 2.1.3 WebAssembly’s runtime 2.1.4 WebAssembly’s control-flow 2.1.5 Security and reliability for WebAssembly 2.1.6 Open challenges 2.2 Software diversification 2.2.1 Automatic generation of software variants 2.2.2 Equivalence Checking 2.2.3 Variants deployment 2.2.4 Measuring Software Diversification

Suggestions

  • Divers

Defensive

delete 'Defensive' by ''

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Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

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Results

delete 'Results' by ''

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Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Suggestions

Methodology

delete 'Methodology' by ''

Happy suggestion

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Suggestions

evasion

delete 'evasion' by ''

Happy suggestion

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Suggestions

defense

delete 'defense' by ''

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Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Suggestions

Malware

delete 'Malware' by ''

Happy suggestion

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Suggestions

Diversification:

delete 'Diversification:' by ''

Happy suggestion

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions

Suggestions

applications

delete 'applications' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Security

delete 'Security' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

and

delete 'and' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

MEWE,

delete 'MEWE,' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

CROW,

delete 'CROW,' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Comparing

delete 'Comparing' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Exemplifying

delete 'Exemplifying' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

traversal

delete 'traversal' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

E-Graph

delete 'E-Graph' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Rules

delete 'Rules' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Rewriting

delete 'Rewriting' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

for

delete 'for' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Diversification

delete 'Diversification' by ''

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

Binary

replace 'Binary' by 'WebAssembl'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • WebAssembl

Effective

replace 'Effective' by 'for'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • for

and

replace 'and' by 'Diversification'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • Diversification

Fast

replace 'Fast' by 'Binary'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • Binary

WASM-MUTATE:

replace 'WASM-MUTATE:' by 'Effective'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • Effective

binary

replace 'binary' by 'and'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • and

a

replace 'a' by 'WASM-MUTATE:'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • WASM-MUTATE:

Multi-variant

replace 'Multi-variant' by 'Multivariant'

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • Multivariant

WebAssembly WebAssembly

Possible typo: you repeated a word

Happy suggestion

Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications

Suggestions

  • WebAssembly

Diversification

replace 'Diversification' by 'diversification.'

Happy suggestion

2.2.5 Offensive or defensive assessment of diversification 2.3 Open challenges for software diversification.

2.2.5 Offensive or Defensive assessment of diversification 2.3 Open challenges for Software Diversification

Suggestions

  • diversification.

Software

replace 'Software' by 'software'

Happy suggestion

2.2.5 Offensive or defensive assessment of diversification 2.3 Open challenges for software diversification.

2.2.5 Offensive or Defensive assessment of diversification 2.3 Open challenges for Software Diversification

Suggestions

  • software

Defensive

replace 'Defensive' by 'defensive'

Happy suggestion

2.2.5 Offensive or defensive assessment of diversification 2.3 Open challenges for software diversification.

2.2.5 Offensive or Defensive assessment of diversification 2.3 Open challenges for Software Diversification

Suggestions

  • defensive

Diversification

replace 'Diversification' by 'Diversification.'

Happy suggestion

Conclusions and Future Work 5.1 5.2 5.3 5.3.1 5.3.2 5.3.3 Summary of technical contributions Key results of the thesis Future Work Data augmentation for Machine Learning on WebAssembly programs Improving WebAssembly malware detection via canonicalization Oneshot Diversification.

Conclusions and Future Work 5.1 5.2 5.3 5.3.1 5.3.2 5.3.3 Summary of technical contributions Key results of the thesis Future Work Data augmentation for Machine Learning on WebAssembly programs Improving WebAssembly malware detection via canonicalization Oneshot Diversification

Suggestions

  • Diversification.

insert '' by '123'

Happy suggestion

123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123

123

Suggestions

  • 123

121

replace '121' by '121.'

Happy suggestion

121.

121

Suggestions

  • 121.

Edge

replace 'Edge' by 'Edge.'

Happy suggestion

Multi-Variant Execution at the Edge.

Multi-Variant Execution at the Edge

Suggestions

  • Edge.

119

replace '119' by '119.'

Happy suggestion

119.

119

Suggestions

  • 119.

117

replace '117' by '117.'

Happy suggestion

117.

117

Suggestions

  • 117.

115

replace '115' by '115.'

Happy suggestion

WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly 115.

WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly 115

Suggestions

  • 115.

Evasion

replace 'Evasion' by 'Evasion.'

Happy suggestion

WebAssembly Diversification for Malware Evasion.

WebAssembly Diversification for Malware Evasion

Suggestions

  • Evasion.

113

replace '113' by '113.'

Happy suggestion

113.

113

Suggestions

  • 113.

109

replace '109' by '109.'

Happy suggestion

109.

109

Suggestions

  • 109.

papers

replace 'papers' by 'papers.'

Happy suggestion

II Included papers.

II Included papers

Suggestions

  • papers.

References

replace 'References' by 'References.'

Happy suggestion

References.

References

Suggestions

  • References.

Traces

replace 'Traces' by 'Traces.'

Happy suggestion

Scalable Comparison of JavaScript V8 Bytecode Traces.

Scalable Comparison of JavaScript V8 Bytecode Traces

Suggestions

  • Traces.

I

delete 'I' by ''

Happy suggestion

Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part

Part I

Suggestions

insert '' by '-'

Happy suggestion

Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part

Part I

Suggestions

  • -

insert '' by 'I'

Happy suggestion

Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part

Part I

Suggestions

  • I

insert '' by 'Part'

Happy suggestion

Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part

Part I

Suggestions

  • Part

insert '' by '-'

Happy suggestion

Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis

Thesis

Suggestions

  • -

insert '' by 'Thesis'

Happy suggestion

Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis

Thesis

Suggestions

  • Thesis

-all

delete '-all' by ''

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

marriage

replace 'marriage' by 'all'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • all

happy

replace 'happy' by '-'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • -

a

replace 'a' by 'marriage'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • marriage

punishment;

replace 'punishment;' by 'happy'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • happy

of

replace 'of' by 'a'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • a

dénouement

replace 'dénouement' by 'punishment;'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • punishment;

a

replace 'a' by 'of'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • of

not;

replace 'not;' by 'denouement'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • denouement

what

replace 'what' by 'a'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • a

or

replace 'or' by 'not;'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • not;

show;

replace 'show;' by 'what'

Happy suggestion

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.

Suggestions

  • what

its

delete 'its' by ''

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

At

delete 'At' by ''

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

[2].

replace '[2].' by 'best.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • best.

1990

replace '1990' by 'its'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • its

in

replace 'in' by 'At'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • At

appearance

replace 'appearance' by '[2].'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • [2].

its

replace 'its' by '1990'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • 1990

made

replace 'made' by 'in'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • in

Nexus,

replace 'Nexus,' by 'appearance'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • appearance

browser,

replace 'browser,' by 'its'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • its

web

replace 'web' by 'made'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • made

first

replace 'first' by 'Nexus,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Nexus,

TT

replace 'TT' by 'browser,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • browser,

renderers.

replace 'renderers.' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

document

replace 'document' by 'first'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • first

PDF

replace 'PDF' by 'TT's'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • TT's

and

replace 'and' by 'renderers.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • renderers.

creators,

replace 'creators,' by 'document'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • document

animation

replace 'animation' by 'PDF'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • PDF

players,

replace 'players,' by 'and'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • and

audio

replace 'audio' by 'creators,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • creators,

and

replace 'and' by 'animation'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • animation

video

replace 'video' by 'players,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • players,

as

replace 'as' by 'audio'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • audio

such

replace 'such' by 'and'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • and

applications,

replace 'applications,' by 'video'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • video

comprehensive

replace 'comprehensive' by 'as'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • as

running

replace 'running' by 'such'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • such

of

replace 'of' by 'applications,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • applications,

capable

replace 'capable' by 'comprehensive'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • comprehensive

systems

replace 'systems' by 'running'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • running

intricate

replace 'intricate' by 'of'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • of

into

replace 'into' by 'capable'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • capable

evolved

replace 'evolved' by 'systems'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • systems

have

replace 'have' by 'intricate'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • intricate

browsers

replace 'browsers' by 'into'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • into

web

replace 'web' by 'evolved'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • evolved

decades,

replace 'decades,' by 'have'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • have

several

replace 'several' by 'browsers'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • browsers

past

replace 'past' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

the

replace 'the' by 'decades,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • decades,

over

replace 'over' by 'several'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • several

Consequently,

replace 'Consequently,' by 'past'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • past

computers.

replace 'computers.' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

client

replace 'client' by 'over'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • over

on

replace 'on' by 'Consequently,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Consequently,

executed

replace 'executed' by 'computers.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • computers.

is

replace 'is' by 'client'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • client

which

replace 'which' by 'on'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • on

code,

replace 'code,' by 'executed'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • executed

JavaScript

replace 'JavaScript' by 'is'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • is

also

replace 'also' by 'which'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • which

but

replace 'but' by 'code,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • code,

HTML

replace 'HTML' by 'JavaScript'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • JavaScript

only

replace 'only' by 'also'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • also

not

replace 'not' by 'but'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • but

incorporate

replace 'incorporate' by 'HTML'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • HTML

pages

replace 'pages' by 'only'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • only

web

replace 'web' by 'not'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • not

of

replace 'of' by 'incorporate'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • incorporate

majority

replace 'majority' by 'pages'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • pages

the

replace 'the' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

day,

replace 'day,' by 'of'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • of

present

replace 'present' by 'majority'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • majority

In

delete 'In' by ''

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

support.

replace 'support.' by 'day,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • day,

JavaScript

replace 'JavaScript' by 'present'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • present

offer

replace 'offer' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

browsers

replace 'browsers' by 'In'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • In

all

replace 'all' by 'support.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • support.

Netscape,

replace 'Netscape,' by 'JavaScript'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • JavaScript

of

replace 'of' by 'offer'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • offer

advent

replace 'advent' by 'browsers'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • browsers

the

replace 'the' by 'all'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • all

since

replace 'since' by 'Netscape,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Netscape,

Significantly,

replace 'Significantly,' by 'of'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • of

browser.

replace 'browser.' by 'advent'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • advent

web

replace 'web' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

a

replace 'a' by 'since'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • since

with

replace 'with' by 'Significantly,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Significantly,

device

replace 'device' by 'browser.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • browser.

any

replace 'any' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

from

replace 'from' by 'a'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • a

accessible

replace 'accessible' by 'with'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • with

are

replace 'are' by 'device'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • device

and

replace 'and' by 'any'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • any

up-to-date,

replace 'up-to-date,' by 'from'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • from

always

replace 'always' by 'accessible'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • accessible

installation,

delete 'installation,' by ''

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

require

delete 'require' by ''

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

not

replace 'not' by 'and'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • and

do

replace 'do' by 'up-to-date,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • up-to-date,

applications

replace 'applications' by 'always'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • always

web

replace 'web' by 'are'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • are

software,

replace 'software,' by 'installation,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • installation,

native

replace 'native' by 'require'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • require

classical

replace 'classical' by 'not'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • not

unlike

replace 'unlike' by 'do'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • do

benefits:

replace 'benefits:' by 'applications'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • applications

highlighted

replace 'highlighted' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

immediately

replace 'immediately' by 'software,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • software,

content

replace 'content' by 'native'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • native

web

replace 'web' by 'classical'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • classical

Interactive

replace 'Interactive' by 'unlike'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • unlike

client-side.

replace 'client-side.' by 'benefits:'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • benefits:

the

replace 'the' by 'highlighted'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • highlighted

on

replace 'on' by 'immediately'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • immediately

execution

replace 'execution' by 'content'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • content

code

replace 'code' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

allowed

replace 'allowed' by 'Interactive'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Interactive

that

replace 'that' by 'client-side.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • client-side.

language

replace 'language' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

programming

replace 'programming' by 'on'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • on

a

replace 'a' by 'execution'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • execution

[3],

replace '[3],' by 'code'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • code

JavaScript

replace 'JavaScript' by 'allowed'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • allowed

introducing

replace 'introducing' by 'that'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • that

by

replace 'by' by 'language'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • language

concept

replace 'concept' by 'programming'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • programming

this

replace 'this' by 'a'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • a

revolutionized

replace 'revolutionized' by '[3],'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • [3],

browser

replace 'browser' by 'JavaScript'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • JavaScript

Netscape

replace 'Netscape' by 'introducing'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • introducing

the

replace 'the' by 'by'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • by

1995,

replace '1995,' by 'concept'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • concept

In

replace 'In' by 'this'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • this

pages.

replace 'pages.' by 'revolutionized'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • revolutionized

web

replace 'web' by 'browser'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • browser

with

replace 'with' by 'Netscape'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Netscape

conjunction

replace 'conjunction' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

in

replace 'in' by '1995,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • 1995,

code

replace 'code' by 'In'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • In

executing

replace 'executing' by 'pages.'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • pages.

of

replace 'of' by 'web'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • web

concept

replace 'concept' by 'with'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • with

the

replace 'the' by 'conjunction'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • conjunction

to

replace 'to' by 'in'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • in

rise

replace 'rise' by 'code'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • code

gave

replace 'gave' by 'executing'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • executing

experiences

replace 'experiences' by 'of'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • of

user

replace 'user' by 'concept'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • concept

engaging

replace 'engaging' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

more

replace 'more' by 'to'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • to

for

replace 'for' by 'rise'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • rise

demand

replace 'demand' by 'gave'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • gave

the

replace 'the' by 'experiences'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • experiences

and

replace 'and' by 'user'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • user

companies,

replace 'companies,' by 'engaging'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • engaging

internet-based

replace 'internet-based' by 'more'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • more

of

replace 'of' by 'for'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

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  • for

valuation

replace 'valuation' by 'demand'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • demand

insert '' by 'and'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • and

internet,

replace 'internet,' by 'companies,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • companies,

the

replace 'the' by 'internet-based'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • internet-based

proliferation

replace 'proliferation' by 'valuation'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • valuation

insert '' by 'internet,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • internet,

devices,

replace 'devices,' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

insert '' by 'proliferation'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • proliferation

insert '' by 'the'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • the

insert '' by 'devices,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • devices,

insert '' by 'of'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • of

inception,

replace 'inception,' by 'Inception,'

Happy suggestion

Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Inception,

inception

This sentence does not start with an uppercase letter.

Happy suggestion

inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its

Suggestions

  • Inception

side?.

replace 'side?.' by 'side?'

Happy suggestion

Despite being the main scripting language in modern web browsers, JavaScript has limitations due to its unique language characteristics [4]. Each JavaScript engine requires the parsing and recompiling of the JavaScript code, thereby causing substantial overhead. In practice, the process of parsing and compiling JavaScript code constitutes the majority of website load times. Additionally, JavaScript presents security issues, including the lack of memory isolation, which potentially enables information extraction from other processes [5, 6]. Numerous attempts have been made to port other languages, offering different guarantees, to the browser execution as alternatives to JavaScript. For instance, Java applets emerged on web pages in the late 90s, enabling the execution of Java bytecode on the client side? Likewise, Microsoft proposed ActiveX in 1996°, and Silverlight in 2007*. However, these attempts either failed to persist or experienced low adoption, primarily due to security issues and the absence of consensus among the community of browser vendors.

Despite being the main scripting language in modern web browsers, JavaScript has limitations due to its unique language characteristics [4]. Each JavaScript engine requires the parsing and recompiling of the JavaScript code, thereby causing substantial overhead. In practice, the process of parsing and compiling JavaScript code constitutes the majority of website load times. Additionally, JavaScript presents security issues, including the lack of memory isolation, which potentially enables information extraction from other processes [5, 6]. Numerous attempts have been made to port other languages, offering different guarantees, to the browser execution as alternatives to JavaScript. For instance, Java applets emerged on web pages in the late 90s, enabling the execution of Java bytecode on the client side?. Likewise, Microsoft proposed ActiveX in 1996°, and Silverlight in 2007*. However, these attempts either failed to persist or experienced low adoption, primarily due to security issues and the absence of consensus among the community of browser vendors.

Suggestions

  • side?

“nttps://www.microsoft.com/silverlight/

replace '“nttps://www.microsoft.com/silverlight/' by '“nttps://www.microsoft.com/silverlight/.'

Happy suggestion

lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. Archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/.

lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/

Suggestions

  • “nttps://www.microsoft.com/silverlight/.

archive.

replace 'archive.' by 'Archive.'

Happy suggestion

lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. Archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/.

lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/

Suggestions

  • Archive.

lhttps

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lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/

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WebAssembly?.

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Importantly, WebAssembly provides a platform for compiling several legacy code applications, like those written in C/C++. For example, LLVM includes WebAssembly as a backend since release 7.1.0 published in May 2019%. WebAssembly paves the way for web applications to undertake roles traditionally reserved for native desktop applications, having the potential to transform web software as we know it. For example, applications such as AutoCAD and Adobe Photoshop have been ported to WebAssembly?

Importantly, WebAssembly provides a platform for compiling several legacy code applications, like those written in C/C++. For example, LLVM includes WebAssembly as a backend since release 7.1.0 published in May 2019%. WebAssembly paves the way for web applications to undertake roles traditionally reserved for native desktop applications, having the potential to transform web software as we know it. For example, applications such as AutoCAD and Adobe Photoshop have been ported to WebAssembly?.

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to

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The first distinction from earlier attempts to port non-JavaScript languages to the web lies in WebAssembly’s initial design. Unlike its predecessors, WebAssembly was crafted to supplement JavaScript in the browser as a platform-agnostic, low-level bytecode, rather than completely replace it. Its primary goal was to replace computing-intensive JavaScript code in contemporary web applications. Additionally, WebAssembly is the inaugural major language that used formal specification and verification right from the design inception [7, 8].

The first distinction from earlier attempts to port non-JavaScript languages to the web lies in WebAssembly’s initial design. Unlike its predecessors, WebAssembly was crafted to supplement JavaScript in the browser as a platform-agnostic, low-level bytecode, rather than to completely replace it. Its primary goal was to replace computing-intensive JavaScript code in contemporary web applications. Additionally, WebAssembly is the inaugural major language that used formal specification and verification right from the design inception [7, 8].

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the

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Importantly, in 2014, Alon Zakai et al. The Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.

Importantly, in 2014, Alon Zakai et al. proposed the Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.

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Importantly, in 2014, Alon Zakai et al. The Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.

Importantly, in 2014, Alon Zakai et al. proposed the Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.

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9lw

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“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137

“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf 1N7-WmzecAOK4V8R6 9lw

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1N7-WmzecAOK4V8R6

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“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137

“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf 1N7-WmzecAOK4V8R6 9lw

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Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf

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“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137

“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf 1N7-WmzecAOK4V8R6 9lw

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and,

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The extensive applicability and rapid adoption of WebAssembly have prompted requests for additional features. However, these demands do not always align with the initial specifications. For extending WebAssembly with a new proposal, it must satisfy particular criteria. A new proposal needs a formal specification and at least two implementations, e.g., two different WebAssembly engines. This approach allows for swift incorporation of new formalization and features via the so-called “evergreen method” while maintaining the original WebAssembly specification intact. Since the inception of WebAssembly, numerous extensions have been proposed for standardization. For instance, the SIMD proposal enables the execution of vectorized instructions in WebAssembly. After approval, new extensions remain optional, ensuring that the core WebAssembly version remains 1.0. The ongoing development of WebAssembly provides avenues for research and development. However, it also gives rise to security concerns within the ecosystem, as new threats emerge.

The extensive applicability and rapid adoption of WebAssembly have prompted requests for additional features. However, these demands do not always align with the initial specifications. For extending WebAssembly with a new proposal, it must satisfy particular criteria. A new proposal needs a formal specification and, at least two implementations, e.g., two different WebAssembly engines. This approach allows for swift incorporation of new formalization and features via the so-called “evergreen method” while maintaining the original WebAssembly specification intact. Since the inception of WebAssembly, numerous extensions have been proposed for standardization. For instance, the SIMD proposal enables the execution of vectorized instructions in WebAssembly. After approval, new extensions remain optional, ensuring that the core WebAssembly version remains 1.0. The ongoing development of WebAssembly provides avenues for research and development. However, it also gives rise to security concerns within the ecosystem, as new threats emerge.

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self-containment.

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The WebAssembly specification embodies several language design principles that pave the way for its extension beyond the web ecosystem. For instance, the architecture of WebAssembly guarantees self-containedness. Inherently, WebAssembly binaries are prohibited from accessing memory beyond their own designated space, thereby amplifying security via Software Fault Isolation (SFI) policies [9]. Consequently, research has highlighted the benefits of integrating WebAssembly as an intermediate layer in contemporary cloud platforms [10]. In particular, the employment of WebAssembly binaries improves startup times and optimizes memory consumption, outperforming virtualization and containerization [11]. Furthermore, compared to virtual machines and containers, WebAssembly programs are more compact, highlighting their efficient deployment, especially when network transportation is a consideration. The methodology for standalone WebAssembly execution was formalized in 2019 when the Bytecode Alliance proposed the WebAssembly System Interface (WASI)?°. WASI standardizes the execution of WebAssembly via a POSIX-like interface protocol, thereby facilitating the execution of WebAssembly closer to operating systems. This standardization enables WebAssembly to function outside web browsers, extending its use to cloud environments and IoT devices [12, 13].

The WebAssembly specification embodies several language design principles that pave the way for its extension beyond the web ecosystem. For instance, the architecture of WebAssembly guarantees self-containment. Inherently, WebAssembly binaries are prohibited from accessing memory beyond their own designated space, thereby amplifying security via Software Fault Isolation (SFI) policies [9]. Consequently, research has highlighted the benefits of integrating WebAssembly as an intermediate layer in contemporary cloud platforms [10]. In particular, the employment of WebAssembly binaries improves startup times and optimizes memory consumption, outperforming virtualization and containerization [11]. Furthermore, compared to virtual machines and containers, WebAssembly programs are more compact, highlighting their efficient deployment, especially when network transportation is a consideration. The methodology for standalone WebAssembly execution was formalized in 2019 when the Bytecode Alliance proposed the WebAssembly System Interface (WASI)?°. WASI standardizes the execution of WebAssembly via a POSIX-like interface protocol, thereby facilitating the execution of WebAssembly closer to operating systems. This standardization enables WebAssembly to function outside web browsers, extending its use to cloud environments and IoT devices [12, 13].

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//github.com/WebAssembly/WASI

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l0ntps: //github.com/WebAssembly/WASI.

l0nttps: //github.com/WebAssembly/WASI

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l0nttps:

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l0ntps: //github.com/WebAssembly/WASI.

l0nttps: //github.com/WebAssembly/WASI

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l0nttps

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l0nttps: //github.com/WebAssembly/WASI

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attack!!.

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The above issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!

The aforementioned issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!.

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aforementioned

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The above issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!

The aforementioned issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!.

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ecosystems

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Predictability in WebAssembly ecosystems.

Predictability in WebAssembly ecosystems

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l1nttps

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l1nttps://www.fastly.com/blog/defense-in-depth-stopping-a-wasm-compiler-bug-befo re-it-became-a-problem

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the

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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demonstrated

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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al.

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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et

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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Genkin

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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deployments,

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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standalone

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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In

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • standalone

[21].

replace '[21].' by 'In'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • In

executions

replace 'executions' by '[21].'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • [21].

during

replace 'during' by 'WebAssembly'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • WebAssembly

crashes

replace 'crashes' by 'during'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • during

and

replace 'and' by 'crashes'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • crashes

overflows

replace 'overflows' by 'and'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • and

unnoticed

replace 'unnoticed' by 'overflows'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • overflows

in

replace 'in' by 'unnoticed'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • unnoticed

result

replace 'result' by 'in'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • in

could

replace 'could' by 'result'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • result

protections

replace 'protections' by 'could'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • could

stack-smashing

replace 'stack-smashing' by 'protections'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • protections

of

replace 'of' by 'stack-smashing'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • stack-smashing

lack

replace 'lack' by 'of'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • of

the

replace 'the' by 'lack'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • lack

Besides,

replace 'Besides,' by 'the'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • the

code.

replace 'code.' by 'Besides,'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • Besides,

source

replace 'source' by 'code.'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • code.

the

replace 'the' by 'source'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • source

in

replace 'in' by 'the'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • the

flaws

replace 'flaws' by 'in'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • in

to

replace 'to' by 'flaws'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • flaws

due

replace 'due' by 'to'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • to

binaries

replace 'binaries' by 'due'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • due

in

replace 'in' by 'WebAssembly'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • WebAssembly

exist

replace 'exist' by 'in'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • in

can

replace 'can' by 'exist'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • exist

vulnerabilities

replace 'vulnerabilities' by 'can'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • can

innate

replace 'innate' by 'vulnerabilities'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • vulnerabilities

as

replace 'as' by 'innate'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • innate

20]

replace '20]' by 'as'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • as

[19,

replace '[19,' by '20]'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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  • 20]

feasible

replace 'feasible' by '[19,'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • [19,

are

replace 'are' by 'feasible'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • feasible

itself

replace 'itself' by 'are'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • are

of

replace 'of' by 'WebAssembly'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • WebAssembly

memory

replace 'memory' by 'of'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • of

the

replace 'the' by 'memory'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • memory

within

replace 'within' by 'the'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • the

attacks

replace 'attacks' by 'within'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • within

Moreover,

replace 'Moreover,' by 'attacks'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • attacks

feasible.

replace 'feasible.' by 'Moreover,'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • Moreover,

always

replace 'always' by 'feasible.'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • feasible.

not

replace 'not' by 'always'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • always

are

replace 'are' by 'not'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • not

which

replace 'which' by 'are'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • are

changes,

replace 'changes,' by 'which'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • which

operating-level

replace 'operating-level' by 'changes,'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • changes,

and

replace 'and' by 'operating-level'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • operating-level

hardware

replace 'hardware' by 'and'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • and

involve

replace 'involve' by 'hardware'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • hardware

often

replace 'often' by 'involve'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • involve

mitigations

replace 'mitigations' by 'often'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • often

cases,

replace 'cases,' by 'mitigations'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • mitigations

such

replace 'such' by 'cases,'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • cases,

In

replace 'In' by 'such'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • such

[18].

replace '[18].' by 'In'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • In

browsers

replace 'browsers' by '[18].'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • [18].

in

replace 'in' by 'browsers'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • browsers

malware

replace 'malware' by 'in'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • in

using

replace 'using' by 'WebAssembly'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • WebAssembly

attacks

replace 'attacks' by 'using'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • using

side-channel

replace 'side-channel' by 'attacks'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • attacks

contention

replace 'contention' by 'side-channel'

Happy suggestion

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

Suggestions

  • side-channel

port

replace 'port' by 'contention'

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].

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Approach: Software Diversification.

Approach: Software Diversification

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To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given that all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware poses a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:

To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware pose a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:

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To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given that all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware poses a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:

To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware pose a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:

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Problem Statements.

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This dissertation introduces tools, strategies, and methodologies designed to address the previously enumerated problems via Software Diversification. Software Diversification is a security strategy that involves identifying, developing, and deploying program variants of a given original program [32]. Pioneers in this field, Cohen et al. [33] and Forrest et al. [34], proposed enhancing software diversity through code transformations. Their proposal recommended the creation of diverse program variants, maintaining their original functionalities. Software Diversification aims to lessen potential vulnerabilities by enhancing behavior unpredictability in observabilities used to conduct attacks, e.g., side-channels.

This dissertation introduces tools, strategies, and methodologies designed to address the previously enunciated problems via Software Diversification. Software Diversification is a security strategy that involves identifying, developing, and deploying program variants of a given original program [32]. Pioneers in this field, Cohen et al. [33] and Forrest et al. [34], proposed enhancing software diversity through code transformations. Their proposal recommended the creation of diverse program variants, maintaining their original functionalities. Software Diversification aims to lessen potential vulnerabilities by enhancing behavior unpredictability in observabilities used to conduct attacks, e.g., side-channels.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.

C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.

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In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

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In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

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In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

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In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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1989.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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diversification

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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underscored

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Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.

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Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w C2 Defensive diversification technique | WY WW C3 Extensive experimental evaluation | WY WY WY WW

Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w~ C2 Defensive diversification technique | ~ WY WW C3 Extensive experimental evaluation | WY WY WY WW

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w~

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Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w C2 Defensive diversification technique | WY WW C3 Extensive experimental evaluation | WY WY WY WW

Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w~ C2 Defensive diversification technique | ~ WY WW C3 Extensive experimental evaluation | WY WY WY WW

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technique

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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proposed

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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each

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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evaluation:

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.

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II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)

II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007

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II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)

II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007

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3564007

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II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)

II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007

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Summary In this paper, we introduce the first entirely automated workflow for diversifying WebAssembly binaries. We present CROW, an open-source tool that implements Software Diversification through enumerative synthesis. We assess the capabilities of CROW and examine its application on real-world, security-sensitive programs. In general, CROW can create thousands of statically diverse variants. Furthermore, we illustrate that the generated variants exhibit different behaviors at runtime.

Summary: In this paper, we introduce the first entirely automated workflow for diversifying WebAssembly binaries. We present CROW, an open-source tool that implements Software Diversification through enumerative synthesis. We assess the capabilities of CROW and examine its application on real-world, security-sensitive programs. In general, CROW can create thousands of statically diverse variants. Furthermore, we illustrate that the generated variants exhibit different behaviors at runtime.

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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https://doi.org/10.14722/madweb.

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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12

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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2021),

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

(MADWeb

replace '(MADWeb' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Web

replace 'Web' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

the

replace 'the' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

for

replace 'for' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Defenses

replace 'Defenses' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

and

replace 'and' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Attacks,

replace 'Attacks,' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Measurements,

replace 'Measurements,' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Measurements

replace 'Measurements' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

on

replace 'on' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Workshop

replace 'Workshop' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Martin

replace 'Martin' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Baudry,

replace 'Baudry,' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Benoit

replace 'Benoit' by '23004'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 23004

Vera-Pérez,

replace 'Vera-Pérez,' by '2021.'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 2021.

Oscar

replace 'Oscar' by 'https://doi.org/10.14722/madweb.'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • https://doi.org/10.14722/madweb.

Floros,

replace 'Floros,' by 'pages'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • pages

Orestis

replace 'Orestis' by '12'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 12

Cabrera-Arteaga,

replace 'Cabrera-Arteaga,' by '2021),'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • 2021),

Javier

replace 'Javier' by '(MADWeb'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • (MADWeb

bytecode.

replace 'bytecode.' by 'Web'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Web

insert '' by 'Defenses'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Defenses

insert '' by 'and'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • and

insert '' by 'Attacks,'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Attacks,

insert '' by 'Measurements,'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Measurements,

insert '' by 'Measurements'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Measurements

insert '' by 'on'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • on

insert '' by 'Workshop'

Happy suggestion

CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

Suggestions

  • Workshop

insert '' by 'Monperrus'

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230

I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004

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1.1

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This compilation thesis comprises the following research papers. In Table 1.1, we map the contributions to our research papers.

This compilation thesis comprises the following research papers. In Table 1.1 we map the contributions to our research papers.

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papers

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Summary of research papers.

Summary of research papers

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In this paper, we synthesize functionally equivalent variants of deployed edge services. Service variants are encapsulated into a single multivariant WebAssembly binary. A random variant is selected and executed each time a function is invoked. Execution of multivariant binaries occurs on the global edge platform provided by Fastly, as part of a research collaboration. We demonstrate that multivariant binaries present a diverse range of execution traces throughout the entire edge platform, distributed worldwide, effectively creating a moving target defense.

Summary: In this paper, we synthesize functionally equivalent variants of deployed edge services. Service variants are encapsulated into a single multivariant WebAssembly binary. A random variant is selected and executed each time a function is invoked. Execution of multivariant binaries occurs on the global edge platform provided by Fastly, as part of a research collaboration. We demonstrate that multivariant binaries present a diverse range of execution traces throughout the entire edge platform, distributed worldwide, effectively creating a moving target defense.

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IV. WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages.

IV: WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages

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IV. WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages.

IV: WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages

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This paper introduces WASM-MUTATE, a compiler-agnostic WebAssembly diversification engine. The engine is designed to swiftly generate functionally equivalent yet behaviorally diverse WebAssembly variants by randomly traversing e-graphs. E-graphs are specific graph data structures for representing and applying rewriting rules. We show that WASM-MUTATE can generate tens of thousands of unique WebAssembly variants in minutes. Importantly, WASM-MUTATE can safeguard WebAssembly binaries from timing side-channel attacks, such as Spectre.

Summary: This paper introduces WASM-MUTATE, a compiler-agnostic WebAssembly diversification engine. The engine is designed to swiftly generate functionally equivalent yet behaviorally diverse WebAssembly variants by randomly traversing e-graphs. E-graphs are specific graph data structures for representing and applying rewriting rules. We show that WASM-MUTATE can generate tens of thousands of unique WebAssembly variants in minutes. Importantly, WASM-MUTATE can safeguard WebAssembly binaries from timing side-channel attacks, such as Spectre.

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Ill: WASM-MUTATE: Fast and efficient Software Diversification for WebAssembly. Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.

Ill: WASM-MUTATE: Fast and efficient Software Diversification for WebAssembly. Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324

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This dissertation comprises two parts as a compilation thesis. Part one summarizes the research papers included within, which is partially rooted in the author’s licentiate thesis [40]. Chapter 2 offers a background on WebAssembly and the latest advancements in Software Diversification. Chapter 3 delves into our technical contributions. Chapter 4 exhibits two use cases applying our technical contributions. Chapter 5 concludes the thesis and outlines future research directions. The second part of this thesis incorporates all the papers discussed in part one.

This dissertation comprises two parts as a compilation thesis. Part one summarises the research papers included within, which is partially rooted in the author’s licentiate thesis [40]. Chapter 2 offers a background on WebAssembly and the latest advancements in Software Diversification. Chapter 3 delves into our technical contributions. Chapter 4 exhibits two use cases applying our technical contributions. Chapter 5 concludes the thesis and outlines future research directions. The second part of this thesis incorporates all the papers discussed in part one.

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This dissertation comprises two parts as a compilation thesis. Part one summarises the research papers included within, which is partially rooted in the author’s licentiate thesis [40]. Chapter 2 offers a background on WebAssembly and the latest advancements in Software Diversification. Chapter 3 delves into our technical contributions. Chapter 4 exhibits two use cases applying our technical contributions. Chapter 5 concludes the thesis and outlines future research directions. The second part of this thesis incorporates all the papers discussed in part one.

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Software Diversification. In Section 2.1, we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. The chapter discusses the state-of-the-art in the areas of WebAssembly and WebAssembly.

Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and

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Software Diversification. In Section 2.1, we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. The chapter discusses the state-of-the-art in the areas of WebAssembly and WebAssembly.

Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and

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Software Diversification. In Section 2.1, we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. The chapter discusses the state-of-the-art in the areas of WebAssembly and WebAssembly.

Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and

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Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and

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— J.R.R. Tolkien's Tolkien.

— J.R.R. Tolkien

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— J.R.R. Tolkien's Tolkien.

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You must have a map, no matter how rough. Otherwise, you wander all over the place.

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You must have a map, no matter how rough. Otherwise you wander all over the place.

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ART

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BACKGROUND AND STATE OF THE ART.

BACKGROUND AND STATE OF THE ART

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The WAT text format is primarily designed for human readability and for low-level manual editing.

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There are several compilers that turn source code into WebAssembly binaries. For example, LLVM compiles to WebAssembly as a backend option since its 7.1.0 release in early 20197, supporting a diverse set of frontend languages like C/C++, Rust, Go, and AssemblyScript®. Significantly, a study by Hilbig [45] reveals that 70% of WebAssembly binaries are generated using LLVM-based compilers. The main advantage of using LLVM is that it provides a modular and state-of-the-art optimization infrastructure for WebAssembly binaries. Today, Emscripten* is the most frequently used tool for porting C/C++ code to the Web as a drop-in replacement for a standard compiler like gcc or clang. The main advantage of Emscripten is that it provides a complete toolchain for compiling C/C++ code to WebAssembly, including the automatic generation of external functions for interacting with a Web host environment. Recently, the Kotlin Multiplatform framework® has incorporated WebAssembly as a compilation target, enabling the compilation of Kotlin code to WebAssembly. Similarly, the Cheerp® project proposes a Java Virtual Machine(JVM) fully ported to WebAssembly, supporting Java applications and legacy applets in the browser.

There are several compilers that turn source code into WebAssembly binaries. For example, LLVM compiles to WebAssembly as a backend option since its 7.1.0 release in early 20197, supporting a diverse set of frontend languages like C/C++, Rust, Go, and AssemblyScript®. Significantly, a study by Hilbig [45] reveals that 70% of WebAssembly binaries are generated using LLVM-based compilers. The main advantage of using LLVM is that it provides a modular and state-of-the-art optimization infrastructure for WebAssembly binaries. Today, Emscripten* is the most frequently used tool for porting C/C++ code to the Web as a drop-in replacement for a standard compiler like gcc or clang. The main advantage of Emscripten is that it provides a complete toolchain for compiling C/C++ code to WebAssembly, including the automatic generation of the external functions for interacting with a Web host environment. Recently, the Kotlin Multiplatform framework® has incorporated WebAssembly as a compilation target, enabling the compilation of Kotlin code to WebAssembly. Similarly, the Cheerp® project proposes a Java Virtual Machine(JVM) fully ported to WebAssembly, supporting Java applications and legacy applets in the browser.

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There are several compilers that turn source code into WebAssembly binaries. For example, LLVM compiles to WebAssembly as a backend option since its 7.1.0 release in early 20197, supporting a diverse set of frontend languages like C/C++, Rust, Go, and AssemblyScript®. Significantly, a study by Hilbig [45] reveals that 70% of WebAssembly binaries are generated using LLVM-based compilers. The main advantage of using LLVM is that it provides a modular and state-of-the-art optimization infrastructure for WebAssembly binaries. Today, Emscripten* is the most frequently used tool for porting C/C++ code to the Web as a drop-in replacement for a standard compiler like gcc or clang. The main advantage of Emscripten is that it provides a complete toolchain for compiling C/C++ code to WebAssembly, including the automatic generation of the external functions for interacting with a Web host environment. Recently, the Kotlin Multiplatform framework® has incorporated WebAssembly as a compilation target, enabling the compilation of Kotlin code to WebAssembly. Similarly, the Cheerp® project proposes a Java Virtual Machine(JVM) fully ported to WebAssembly, supporting Java applications and legacy applets in the browser.

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*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

Suggestions

  • Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

replace 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html'

Happy suggestion

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

Suggestions

  • Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html

html

replace 'html' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html'

Happy suggestion

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

Suggestions

  • Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html

Shttps://kotlinlang.org/docs/wasm-overview.

replace 'Shttps://kotlinlang.org/docs/wasm-overview.' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html'

Happy suggestion

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

Suggestions

  • Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html

insert '' by 'Shttps://kotlinlang.org/docs/wasm-overview.'

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*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

Suggestions

  • Shttps://kotlinlang.org/docs/wasm-overview.

insert '' by 'html'

Happy suggestion

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.

*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

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  • html

Unpaired symbol: ‘”’ seems to be missing

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*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive

Suggestions

insert '' by ';'

Happy suggestion

Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;

block ; label @2

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  • ;

insert '' by '@2'

Happy suggestion

Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;

block ; label @2

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  • @2

insert '' by 'label'

Happy suggestion

Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;

block ; label @2

Suggestions

  • label

block

replace 'block' by 'Block'

Happy suggestion

Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;

block ; label @2

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  • Block

block

This sentence does not start with an uppercase letter.

Happy suggestion

block ; label @2

Suggestions

  • Block

insert '' by 'loc'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

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  • loc

insert '' by 'in'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • in

insert '' by 'saved'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • saved

insert '' by 'is'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • is

insert '' by 'which'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • which

insert '' by 'element'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • element

insert '' by 'last'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • last

insert '' by 'the'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • the

insert '' by 'Return'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • Return

insert '' by ';'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • ;

insert '' by '3'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • 3

insert '' by 'end'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • end

insert '' by 'O'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • O

insert '' by 'locpl'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • locpl

local.get

replace 'local.get' by 'Local.get'

Happy suggestion

Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • Local.get

local

This sentence does not start with an uppercase letter.

Happy suggestion

local.get 0 return ; Return the last element which is saved in locpl O end 3

Suggestions

  • Local

132.store

delete '132.store' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

end

delete 'end' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

0

delete '0' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

¢)

delete '¢)' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

Celine

delete 'Celine' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

"...

delete '"...' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

"name"

delete '"name"' by ''

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

(@custom

replace '(@custom' by ')'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • )

"

replace '"' by 'Celine'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • Celine

"__wbg__"

replace '"__wbg__"' by '"...'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • "...

(import

replace '(import' by '"name"'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • "name"

"

replace '"' by '(@custom'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • (@custom

"__wbg__"

replace '"__wbg__"' by '"'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • "

(import

replace '(import' by '"__wbg__"'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • "__wbg__"

¢

replace '¢' by '(import'

Happy suggestion

OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get

OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store

Suggestions

  • (import

example.

delete 'example.' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

code

delete 'code' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

Rust

delete 'Rust' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

the

delete 'the' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

for

delete 'for' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

2.1

delete '2.1' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

Listing

delete 'Listing' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

to

delete 'to' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

Refer

delete 'Refer' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

2.2:

delete '2.2:' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

"...")

delete '"...")' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

"producers"

delete '"producers"' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(@custom

delete '(@custom' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

args...")

delete 'args...")' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

"invalid

delete '"invalid' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

1048576)

delete '1048576)' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(132.const

delete '(132.const' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

()

delete '()' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(data

delete '(data' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

45))

delete '45))' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(func

delete '(func' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

"main"

delete '"main"' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(export

delete '(export' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

40))

delete '40))' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

"fibo"

delete '"fibo"' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

0))

delete '0))' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(memory

delete '(memory' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

"memory"

delete '"memory"' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

1048576))

delete '1048576))' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

(€i32.const

delete '(€i32.const' by ''

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

i32)

replace 'i32)' by '('

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • (

(mut

replace '(mut' by '1048576))'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • 1048576))

()

replace '()' by '(€i32.const'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • (€i32.const

(global

replace '(global' by 'i32)'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • i32)

17)

replace '17)' by '(mut'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • (mut

insert '' by '(global'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • (global

(memory

replace '(memory' by '17)'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • 17)

funcref)

replace 'funcref)' by '()'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • ()

33

replace '33' by '(memory'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • (memory

33

replace '33' by 'funcref)'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • funcref)

()

replace '()' by '33'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • 33

(table

replace '(table' by '33'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • 33

offset=20

replace 'offset=20' by '()'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • ()

i32.store

replace 'i32.store' by '(table'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • (table

40

replace '40' by 'offset=20'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • offset=20

call

replace 'call' by 'i32.store'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • i32.store

44

replace '44' by '40'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • 40

insert '' by '44'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • 44

i32)

replace 'i32)' by 'call'

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132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • call

i32

replace 'i32' by 'i32)'

Happy suggestion

132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

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  • i32)

insert '' by 'i32'

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132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

Suggestions

  • i32

¢)

replace '¢)' by ')'

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132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.

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  • )

2.1

replace '2.1' by '2.1,'

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The Wasm binary format is close to machine code and already optimized, being a consecutive collection of sections. In Figure 2.1, we show the binary format of a Wasm section. A Wasm section starts with a 1-byte section ID, followed by a 4-byte section size, and concludes with the section content, which precisely matches the size indicated earlier. A WebAssembly binary contains sections of 13 types, each with a specific semantic role and placement within the module. For instance, the Custom Section stores metadata like the compiler used to generate the binary, while the Type Section contains function signatures that serve to validate the Function Section. The Import Section lists elements imported from the host, and the Function Section details the functions defined within the binary. Other sections like Table, Memory, and Global Sections specify the structure for indirect calls, unmanaged linear memories, and global variables, respectively. Export, Start, Element, Code, Data, and Data Count Sections handle aspects ranging from declaring elements for host engine access to initializing program state, declaring bytecode instructions per function, and initializing linear memory. Each of these sections must occur only once in a binary and can be empty. For clarity, we also annotate sections as comments in the Wasm code in Listing 2.2.

The Wasm binary format is close to machine code and already optimized, being a consecutive collection of sections. In Figure 2.1 we show the binary format of a Wasm section. A Wasm section starts with a 1-byte section ID, followed by a 4-byte section size, and concludes with the section content, which precisely matches the size indicated earlier. A WebAssembly binary contains sections of 13 types, each with a specific semantic role and placement within the module. For instance, the Custom Section stores metadata like the compiler used to generate the binary, while the Type Section contains function signatures that serve to validate the Function Section. The Import Section lists elements imported from the host, and the Function Section details the functions defined within the binary. Other sections like Table, Memory, and Global Sections specify the structure for indirect calls, unmanaged linear memories, and global variables, respectively. Export, Start, Element, Code, Data, and Data Count Sections handle aspects ranging from declaring elements for host engine access to initializing program state, declaring bytecode instructions per function, and initializing linear memory. Each of these sections must occur only once in a binary and can be empty. For clarity, we also annotate sections as comments in the Wasm code in Listing 2.2.

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  • 2.1,

format

replace 'format' by 'format.'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • format.

insert '' by 'the'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • the

insert '' by 'as'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • as

insert '' by 'same'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • same

insert '' by 'is'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • is

insert '' by 'format'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • format

insert '' by 'binary'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

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  • binary

insert '' by 'WebAssembly’s'

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WebAssembly’s binary format is the same as the WebAssembly’s binary format.

WebAssembly’s binary format

Suggestions

  • WebAssembly’s

Javy”

replace 'Javy”' by '“Javy”'

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A recent trend in the WebAssembly ecosystem involves porting various programming languages by converting both the language’s engine or interpreter and the source code into a WebAssembly program. For example, “Javy” encapsulates JavaScript code within the QuickJS interpreter, demonstrating that direct source code conversion to WebAssembly isn’t always required. If an interpreter for a specific language can be compiled to WebAssembly, it allows for the bundling of both the interpreter and the language into a single, isolated WebAssembly binary. Similarly, Blazor® facilitates the execution of INET Common Intermediate Language (CIL) in WebAssembly binaries for browser-based applications. However, packaging the interpreter and the code in one single standalone WebAssembly binary is still immature and faces challenges. For example, the absence of JIT compilation for the “interpreted” code makes it less suitable for long-running tasks [46, 47]. On the other hand, it proves effective for short-running tasks, particularly those executed in Edge-Cloud computing platforms.

A recent trend in the WebAssembly ecosystem involves porting various programming languages by converting both the language’s engine or interpreter and the source code into a WebAssembly program. For example, Javy” encapsulates JavaScript code within the QuickJS interpreter, demonstrating that direct source code conversion to WebAssembly isn’t always required. If an interpreter for a specific language can be compiled to WebAssembly, it allows for the bundling of both the interpreter and the language into a single, isolated WebAssembly binary. Similarly, Blazor® facilitates the execution of INET Common Intermediate Language (CIL) in WebAssembly binaries for browser-based applications. However, packaging the interpreter and the code in one single standalone WebAssembly binary is still immature and faces challenges. For example, the absence of JIT compilation for the “interpreted” code makes it less suitable for long-running tasks [46, 47]. On the other hand, it proves effective for short-running tasks, particularly those executed in Edge-Cloud computing platforms.

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  • “Javy”

Unpaired symbol: ‘“’ seems to be missing

Happy suggestion

A recent trend in the WebAssembly ecosystem involves porting various programming languages by converting both the language’s engine or interpreter and the source code into a WebAssembly program. For example, Javy” encapsulates JavaScript code within the QuickJS interpreter, demonstrating that direct source code conversion to WebAssembly isn’t always required. If an interpreter for a specific language can be compiled to WebAssembly, it allows for the bundling of both the interpreter and the language into a single, isolated WebAssembly binary. Similarly, Blazor® facilitates the execution of INET Common Intermediate Language (CIL) in WebAssembly binaries for browser-based applications. However, packaging the interpreter and the code in one single standalone WebAssembly binary is still immature and faces challenges. For example, the absence of JIT compilation for the “interpreted” code makes it less suitable for long-running tasks [46, 47]. On the other hand, it proves effective for short-running tasks, particularly those executed in Edge-Cloud computing platforms.

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insert '' by 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web'

Happy suggestion

“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web

“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor

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  • Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web

insert '' by 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html'

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“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web

“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor

Suggestions

  • Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html

Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor

replace 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor' by 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html'

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“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web

“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor

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  • Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html

Stack:

delete 'Stack:' by ''

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At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.

Execution Stack: At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.

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Execution

delete 'Execution' by ''

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At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.

Execution Stack: At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.

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runtime

replace 'runtime' by 'runtime.'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • runtime.

insert '' by 'as'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • as

insert '' by 'time'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • time

insert '' by 'same'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • same

insert '' by 'the'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • the

insert '' by 'at'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • at

insert '' by 'runs'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • runs

insert '' by 'runtime'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • runtime

insert '' by 'WebAssembly’s'

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WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.

WebAssembly’s runtime

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  • WebAssembly’s

ID2

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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Content

replace 'Content' by 'ID3.'

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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  • ID3.

insert '' by 'ID2'

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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  • ID2

ID1

replace 'ID1' by 'Content'

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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  • Content

insert '' by 'ID1'

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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  • ID1

i+9+

replace 'i+9+' by 'Size'

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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  • Size

insert '' by 'i+9'

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i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.

i+ i+9 i+9+ Size ID1 Size Content ID2

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  • i+9

i

This sentence does not start with an uppercase letter.

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i+ i+9 i+9+ Size ID1 Size Content ID2

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  • I

org

This sentence does not start with an uppercase letter.

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°nttps://en.wikipedia. org/wiki/LEB128

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  • Org

Functions:

delete 'Functions:' by ''

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At runtime, WebAssembly functions are closures over the module instance, grouping locals and function bodies. Locals are typed variables that are local to a specific function invocation. A function body is a sequence of instructions that are executed when the function is called. Each instruction either reads from the execution stack, writes to the execution stack, reads from the linear memory, writes to the linear memory, reads a global, writes a global or modifies the control-flow of the function. Recalling the example WebAssembly binary, the local variable declarations and typed instructions that are evaluated using the stack can be appreciated between Line 15 and Line 19 in Listing 2.2. When an instruction reads its operands from the stack, it pushes back the result. Notice that, numeric instructions are annotated with their corresponding type.

Functions: At runtime, WebAssembly functions are closures over the module instance, grouping locals and function bodies. Locals are typed variables that are local to a specific function invocation. A function body is a sequence of instructions that are executed when the function is called. Each instruction either reads from the execution stack, writes to the execution stack, reads from the linear memory, writes to the linear memory, reads a global, writes a global or modifies the control-flow of the function. Recalling the example WebAssembly binary, the local variable declarations and typed instructions that are evaluated using the stack can be appreciated between Line 15 and Line 19 in Listing 2.2. When an instruction reads its operands from the stack, it pushes back the result. Notice that, numeric instructions are annotated with their corresponding type.

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when

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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e.g.

delete 'e.g.' by ''

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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level,

replace 'level,' by 'When'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • When

lowest

replace 'lowest' by 'e.g.'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • e.g.

the

replace 'the' by 'level,'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • level,

at

replace 'at' by 'lowest'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • lowest

engine

replace 'engine' by 'the'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • the

host

replace 'host' by 'at'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • at

the

replace 'the' by 'engine'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • engine

by

replace 'by' by 'host'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • host

modified

replace 'modified' by 'the'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • the

exclusively

replace 'exclusively' by 'by'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • by

are

replace 'are' by 'modified'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • modified

that

replace 'that' by 'exclusively'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • exclusively

components

replace 'components' by 'are'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • are

to

replace 'to' by 'that'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • that

refers

replace 'refers' by 'components'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • components

Managed

replace 'Managed' by 'to'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • to

[20].

replace '[20].' by 'refers'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • refers

memory

replace 'memory' by 'Managed'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • Managed

unmanaged

replace 'unmanaged' by '[20].'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • [20].

and

replace 'and' by 'memory'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • memory

managed

replace 'managed' by 'unmanaged'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • unmanaged

as

replace 'as' by 'and'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • and

categorized

replace 'categorized' by 'managed'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • managed

often

replace 'often' by 'as'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • as

is

replace 'is' by 'categorized'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • categorized

responsibility

replace 'responsibility' by 'often'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • often

of

replace 'of' by 'is'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • is

division

replace 'division' by 'responsibility'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • responsibility

This

replace 'This' by 'of'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • of

itself.

replace 'itself.' by 'division'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • division

binary

replace 'binary' by 'This'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • This

the

replace 'the' by 'binary'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • binary

with

replace 'with' by 'WebAssembly'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • WebAssembly

shared

replace 'shared' by 'the'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • the

or

replace 'or' by 'with'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • with

engine

replace 'engine' by 'shared'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • shared

host

replace 'host' by 'or'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • or

solely

replace 'solely' by 'the'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • the

managed

replace 'managed' by 'by'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • by

be

replace 'be' by 'solely'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • solely

either

replace 'either' by 'managed'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • managed

can

replace 'can' by 'be'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • be

components

replace 'components' by 'either'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • either

These

replace 'These' by 'can'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • can

variables.

replace 'variables.' by 'components'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • components

global

replace 'global' by 'These'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • These

and

replace 'and' by 'variables.'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • variables.

variables

replace 'variables' by 'global'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • global

local

replace 'local' by 'and'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • and

memory,

replace 'memory,' by 'variables'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • variables

linear

replace 'linear' by 'local'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • local

components:

replace 'components:' by 'memory,'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • memory,

memory-related

replace 'memory-related' by 'linear'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • linear

of

replace 'of' by 'components:'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • components:

types

replace 'types' by 'memory-related'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • memory-related

key

replace 'key' by 'of'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • of

three

replace 'three' by 'types'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • types

incorporates

replace 'incorporates' by 'key'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • key

instance

replace 'instance' by 'three'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • three

module

replace 'module' by 'incorporates'

Happy suggestion

A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Suggestions

  • incorporates

A

replace 'A' by 'module'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • module

model:

replace 'model:' by 'WebAssembly'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • WebAssembly

Memory

replace 'Memory' by 'A'

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A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.

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  • A

execution:

delete 'execution:' by ''

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While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.

WebAssembly module execution: While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.

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module

delete 'module' by ''

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While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.

WebAssembly module execution: While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.

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15https://github.com/WAVM/WAVM

replace '15https://github.com/WAVM/WAVM' by '//github.com'

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • //github.com

//github.com/bytecodealliance/wasmtime

replace '//github.com/bytecodealliance/wasmtime' by '12ntps:'

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • 12ntps:

M4nttps:

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • llhttps://spidermonkey.dev/

13https://wasmer.io/

replace '13https://wasmer.io/' by '15https://github.com/WAVM/WAVM.git'

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • 15https://github.com/WAVM/WAVM.git

//github.com/wasm3/wasm3

replace '//github.com/wasm3/wasm3' by '//github.com/bytecodealliance/wasmtime'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • //github.com/bytecodealliance/wasmtime

12nttps:

replace '12nttps:' by 'M4ntps:'

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • M4ntps:

llhttps://spidermonkey.dev/

replace 'llhttps://spidermonkey.dev/' by '13https://wasmer.io/'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • 13https://wasmer.io/

googlesource.com/v8/v8.git

replace 'googlesource.com/v8/v8.git' by '//github.com/wasm3/wasm3'

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • //github.com/wasm3/wasm3

//chromium.

replace '//chromium.' by '12ntps:'

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While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

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  • 12ntps:

10nttps:

replace '10nttps:' by 'llhttps://spidermonkey.dev/'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • llhttps://spidermonkey.dev/

WASI.

replace 'WASI.' by 'Googlesource.com/v8/v8.git'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Googlesource.com/v8/v8.git

and

replace 'and' by '//chromium.'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • //chromium.

both

replace 'both' by 'WASI.'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WASI.

support

replace 'support' by 'and'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • and

to

replace 'to' by 'WebAssembly'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WebAssembly

developed

replace 'developed' by 'both'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • both

been

replace 'been' by 'support'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • support

have

replace 'have' by 'to'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • to

[50]

replace '[50]' by 'developed'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • developed

Sledge

replace 'Sledge' by 'been'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • been

and

replace 'and' by 'have'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • have

WAVM",

replace 'WAVM",' by '[50]'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • [50]

Wasmtime!4,

replace 'Wasmtime!4,' by 'Sledge'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Sledge

Wasmer!*,

replace 'Wasmer!*,' by 'and'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • and

WASM3!?,

replace 'WASM3!?,' by 'WAVM"'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WAVM"

as

replace 'as' by 'Wasmtime!4,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Wasmtime!4,

such

replace 'such' by 'Wasmer!*,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Wasmer!*,

engines

replace 'engines' by 'WASM3!?,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WASM3!?,

Standalone

replace 'Standalone' by 'as'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • as

process.

replace 'process.' by 'such'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • such

execution

replace 'execution' by 'engines'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • engines

sandboxed

replace 'sandboxed' by 'Standalone'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Standalone

a

replace 'a' by 'process.'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • process.

ensuring

replace 'ensuring' by 'execution'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • execution

compilation,

replace 'compilation,' by 'sandboxed'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • sandboxed

JIT

replace 'JIT' by 'a'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • a

via

replace 'via' by 'ensuring'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • ensuring

code

replace 'code' by 'compilation,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • compilation,

machine

replace 'machine' by 'JIT'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • JIT

into

replace 'into' by 'via'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • via

translate

replace 'translate' by 'machine'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • machine

often

replace 'often' by 'into'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • into

engines

replace 'engines' by 'WebAssembly'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WebAssembly

these

replace 'these' by 'translate'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • translate

browsers,

replace 'browsers,' by 'often'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • often

to

replace 'to' by 'engines'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • engines

Similarly

replace 'Similarly' by 'these'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • these

48].

replace '48].' by 'browsers,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • browsers,

[49,

replace '[49,' by 'to'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • to

devices

replace 'devices' by 'Similarly'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Similarly

IoT

replace 'IoT' by '48].'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • 48].

and

replace 'and' by '[49,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • [49,

servers,

replace 'servers,' by 'devices'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • devices

cloud,

replace 'cloud,' by 'IoT'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • IoT

the

replace 'the' by 'and'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • and

including

replace 'including' by 'servers,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • servers,

environments,

replace 'environments,' by 'cloud,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • cloud,

of

replace 'of' by 'the'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • the

variety

replace 'variety' by 'including'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • including

a

replace 'a' by 'environments,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • environments,

across

replace 'across' by 'of'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • of

engines

replace 'engines' by 'variety'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • variety

standalone

replace 'standalone' by 'a'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • a

by

replace 'by' by 'across'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • across

executed

replace 'executed' by 'engines'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • engines

be

replace 'be' by 'standalone'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • standalone

then

replace 'then' by 'by'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • by

can

replace 'can' by 'executed'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • executed

binaries

replace 'binaries' by 'be'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • be

These

replace 'These' by 'then'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • then

engines.

replace 'engines.' by 'can'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • can

standalone

replace 'standalone' by 'binaries'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • binaries

in

replace 'in' by 'These'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • These

execution

replace 'execution' by 'engines.'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • engines.

allows

replace 'allows' by 'standalone'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • standalone

which

replace 'which' by 'in'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • in

WASI,

replace 'WASI,' by 'execution'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • execution

implement

replace 'implement' by 'allows'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • allows

that

replace 'that' by 'which'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • which

binaries

replace 'binaries' by 'WASI,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WASI,

generate

replace 'generate' by 'that'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • that

can

replace 'can' by 'binaries'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • binaries

Compilers

replace 'Compilers' by 'WebAssembly'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WebAssembly

environments.

replace 'environments.' by 'generate'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • generate

host

replace 'host' by 'can'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • can

and

replace 'and' by 'Compilers'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • Compilers

modules

replace 'modules' by 'environments.'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • environments.

between

replace 'between' by 'and'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • and

interactions

replace 'interactions' by 'modules'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • modules

for

replace 'for' by 'WebAssembly'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WebAssembly

interface

replace 'interface' by 'between'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • between

POSIX-like

replace 'POSIX-like' by 'interactions'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • interactions

standardized

replace 'standardized' by 'for'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • for

a

replace 'a' by 'interface'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • interface

establishes

replace 'establishes' by 'POSIX-like'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • POSIX-like

[41].

replace '[41].' by 'a'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • a

to

replace 'to' by 'WASI'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WASI

due

replace 'due' by '[41].'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • [41].

primarily

replace 'primarily' by 'WASI'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WASI

evolution,

replace 'evolution,' by 'to'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • to

significant

replace 'significant' by 'due'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • due

undergone

replace 'undergone' by 'primarily'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • primarily

has

replace 'has' by 'evolution,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • evolution,

browsers,

replace 'browsers,' by 'undergone'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • undergone

for

replace 'for' by 'has'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • has

intended

replace 'intended' by 'WebAssembly'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • WebAssembly

initially

replace 'initially' by 'browsers,'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • browsers,

While

replace 'While' by 'for'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • for

engines:

replace 'engines:' by 'intended'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • intended

standalone

replace 'standalone' by 'initially'

Happy suggestion

While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

  • initially

"

Unpaired symbol: ‘"’ seems to be missing

Happy suggestion

WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM

Suggestions

end

delete 'end' by ''

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

instruction;

replace 'instruction;' by 'instruction.'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • instruction.

insert '' by 'the'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • the

insert '' by 'instruction;'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • instruction;

insert '' by 'next'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • next

insert '' by 'to'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • to

insert '' by 'jump'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • jump

insert '' by 'and'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • and

insert '' by 'block'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • block

insert '' by ';)'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • ;)

insert '' by 'Jump'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • Jump

insert '' by 'ena'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • ena

insert '' by 'break'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • break

;

replace ';' by 'instructions'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • instructions

insert '' by ';'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • ;

they

replace 'they' by 'end'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • end

DLoCk

replace 'DLoCk' by 'they'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • they

oe

replace 'oe' by 'DLoCk'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • DLoCk

wees

replace 'wees' by 'oe'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • oe

eeay

replace 'eeay' by 'wees'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • wees

oniee

replace 'oniee' by 'eeay'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • eeay

paint

replace 'paint' by 'oniee'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • oniee

0

replace '0' by 'paint'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • paint

br

replace 'br' by '0'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • 0

the

replace 'the' by 'br'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • br

with

replace 'with' by 'the'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • the

annotated

replace 'annotated' by 'with'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • with

are

replace 'are' by 'annotated'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • annotated

a

replace 'a' by 'are'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • are

instructions

replace 'instructions' by 'a'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • a

Jump

replace 'Jump' by 'instructions'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • instructions

;

replace ';' by 'Jump'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • Jump

1

replace '1' by ';'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • ;

br

replace 'br' by '1'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • 1

Toop

replace 'Toop' by 'br'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • br

block

replace 'block' by 'Toop'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • Toop

insert '' by 'Block'

Happy suggestion

Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • Block

block

This sentence does not start with an uppercase letter.

Happy suggestion

block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end

Suggestions

  • Block

to

delete 'to' by ''

Happy suggestion

A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.

A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting gotos to random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.

Suggestions

gotos

delete 'gotos' by ''

Happy suggestion

A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.

A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting gotos to random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.

Suggestions

insert '' by 'This'

Happy suggestion

The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need. This section reviews the most relevant works in this field. We group them by the technique they employ.

The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need.This section reviews the most relevant works in this field. We group them by the technique they employ.

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The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need. This section reviews the most relevant works in this field. We group them by the technique they employ.

The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need.This section reviews the most relevant works in this field. We group them by the technique they employ.

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analysis:

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SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.

Static analysis: SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.

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Static

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SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.

Static analysis: SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.

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malware:

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Since the introduction of WebAssembly, the Web has consistently experienced an increase in cryptomalware. This rise primarily stems from the shift of mining algorithms from CPUs to WebAssembly, a transition driven by notable performance benefits [66]. Tools such as MineSweeper [26], MinerRay [27], and MINOS [28] employ static analysis with machine learning techniques to detect browser-based cryptomalware. In addition, SEISMIC [29], RAPID [30], and OUTGuard [31] leverage dynamic analysis techniques to achieve a similar objective. Moreover, VirusTotal’®, a tool incorporating over 60 commercial antivirus systems as black-boxes, is capable of detecting cryptomalware in WebAssembly binaries. However, obfuscation studies have exposed their shortcomings, revealing an almost unexplored area for WebAssembly that threatens malware detection accuracy. In concrete, Bhansali et al.’s seminal work [67] demonstrates that cryptomining algorithm’s source code can evade previous techniques through the use of obfuscation techniques.

WebAssembly malware: Since the introduction of WebAssembly, the Web has consistently experienced an increase in cryptomalware. This rise primarily stems from the shift of mining algorithms from CPUs to WebAssembly, a transition driven by notable performance benefits [66]. Tools such as MineSweeper [26], MinerRay [27], and MINOS [28] employ static analysis with machine learning techniques to detect browser-based cryptomalware. In addition, SEISMIC [29], RAPID [30], and OUTGuard [31] leverage dynamic analysis techniques to achieve a similar objective. Moreover, VirusTotal’®, a tool incorporating over 60 commercial antivirus systems as black-boxes, is capable of detecting cryptomalware in WebAssembly binaries. However, obfuscation studies have exposed their shortcomings, revealing an almost unexplored area for WebAssembly that threatens malware detection accuracy. In concrete, Bhansali et al.’s seminal work [67] demonstrates that cryptomining algorithm’s source code can evade previous techniques through the use of obfuscation techniques.

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WebAssembly malware: Since the introduction of WebAssembly, the Web has consistently experienced an increase in cryptomalware. This rise primarily stems from the shift of mining algorithms from CPUs to WebAssembly, a transition driven by notable performance benefits [66]. Tools such as MineSweeper [26], MinerRay [27], and MINOS [28] employ static analysis with machine learning techniques to detect browser-based cryptomalware. In addition, SEISMIC [29], RAPID [30], and OUTGuard [31] leverage dynamic analysis techniques to achieve a similar objective. Moreover, VirusTotal’®, a tool incorporating over 60 commercial antivirus systems as black-boxes, is capable of detecting cryptomalware in WebAssembly binaries. However, obfuscation studies have exposed their shortcomings, revealing an almost unexplored area for WebAssembly that threatens malware detection accuracy. In concrete, Bhansali et al.’s seminal work [67] demonstrates that cryptomining algorithm’s source code can evade previous techniques through the use of obfuscation techniques.

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therefore

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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and

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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execution,

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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secure

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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their

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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enhancing

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • thus

binaries,

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • and

harden

replace 'harden' by 'secure'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • secure

to

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • their

approaches

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • enhancing

exist

replace 'exist' by 'binaries,'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • binaries,

there

replace 'there' by 'WebAssembly'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • WebAssembly

However,

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • harden

binaries.

replace 'binaries.' by 'to'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • to

of

replace 'of' by 'are'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • are

analysis

replace 'analysis' by 'there'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

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  • there

reactive

replace 'reactive' by 'However,'

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The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • However,

on

replace 'on' by 'binaries.'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • binaries.

focused

replace 'focused' by 'WebAssembly'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • WebAssembly

primarily

replace 'primarily' by 'of'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • of

are

replace 'are' by 'analysis'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • analysis

previously

replace 'previously' by 'reactive'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • reactive

discussed

replace 'discussed' by 'on'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • on

techniques

replace 'techniques' by 'focused'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • focused

The

replace 'The' by 'primarily'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • primarily

runtimes:

replace 'runtimes:' by 'are'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • are

and

replace 'and' by 'previously'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • previously

binaries

replace 'binaries' by 'discussed'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • discussed

Protecting

replace 'Protecting' by 'The'

Happy suggestion

The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.

Suggestions

  • The

‘These

delete '‘These' by ''

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

code.

delete 'code.' by ''

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

debugging

replace 'debugging' by 'code.'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • code.

and

replace 'and' by 'WebAssembly'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • WebAssembly

instrumenting,

replace 'instrumenting,' by 'debugging'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • debugging

profiling,

replace 'profiling,' by 'and'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • and

for

replace 'for' by 'instrumenting,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • instrumenting,

used

replace 'used' by 'profiling,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • profiling,

primarily

replace 'primarily' by 'for'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • for

binaries,

replace 'binaries,' by 'used'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • used

for

replace 'for' by 'binaries,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • binaries,

tool

replace 'tool' by 'WebAssembly'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • WebAssembly

analysis

replace 'analysis' by 'for'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • for

dynamic

replace 'dynamic' by 'tool'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • tool

general-purpose

replace 'general-purpose' by 'analysis'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • analysis

only

replace 'only' by 'dynamic'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • dynamic

the

replace 'the' by 'general-purpose'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • general-purpose

remains

replace 'remains' by 'only'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • only

[62]

replace '[62]' by 'the'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • the

Wasabi

replace 'Wasabi' by 'remains'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • remains

However,

replace 'However,' by '[62]'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • [62]

analysis.

replace 'analysis.' by 'Wasabi'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • Wasabi

security

replace 'security' by 'However,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • However,

conducting

replace 'conducting' by 'analysis.'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • analysis.

and

replace 'and' by 'security'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • security

programs,

replace 'programs,' by 'conducting'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • conducting

understanding

replace 'understanding' by 'and'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • and

debugging,

replace 'debugging,' by 'programs,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • programs,

in

replace 'in' by 'understanding'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • understanding

aids

replace 'aids' by 'debugging,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • debugging,

technique

replace 'technique' by 'in'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • in

This

replace 'This' by 'aids'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • aids

61].

replace '61].' by 'technique'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • technique

(ORBS)|[60,

replace '(ORBS)|[60,' by 'This'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • This

Slicing

replace 'Slicing' by '61].'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • 61].

Observational-Based

replace 'Observational-Based' by '(ORBS)|[60,'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • (ORBS)|[60,

on

replace 'on' by 'Slicing'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • Slicing

based

replace 'based' by 'Observational-Based'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • Observational-Based

programs

replace 'programs' by 'on'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • on

slicing

replace 'slicing' by 'programs'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • programs

to

replace 'to' by 'WebAssembly'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • WebAssembly

approach

replace 'approach' by 'slicing'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • slicing

dynamic

replace 'dynamic' by 'to'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • to

a

replace 'a' by 'approach'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • approach

developed

replace 'developed' by 'dynamic'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • dynamic

have

replace 'have' by 'a'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • a

al.

replace 'al.' by 'developed'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • developed

et

replace 'et' by 'Have'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • Have

Stiévenart

replace 'Stiévenart' by 'al.'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • al.

Furthermore,

replace 'Furthermore,' by 'et'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • et

binaries.

replace 'binaries.' by 'Stiévenart'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • Stiévenart

on

replace 'on' by 'binaries.'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • binaries.

analysis

replace 'analysis' by 'WebAssembly'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • WebAssembly

taint

replace 'taint' by 'on'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • on

conducts

replace 'conducts' by 'analysis'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • analysis

which

replace 'which' by 'taint'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • taint

[59],

replace '[59],' by 'conducts'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • conducts

TaintAssembly

replace 'TaintAssembly' by 'which'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • which

as

replace 'as' by '[59],'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • [59],

such

replace 'such' by 'TaintAssembly'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • TaintAssembly

tools

replace 'tools' by 'as'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • as

involves

replace 'involves' by 'such'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • such

analysis

replace 'analysis' by 'tools'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • tools

Dynamic

replace 'Dynamic' by 'involves'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • involves

analysis:

replace 'analysis:' by 'analysis'

Happy suggestion

Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

  • analysis

Unpaired symbol: ‘’’ seems to be missing

Happy suggestion

Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.

Suggestions

diversification

replace 'diversification' by 'diversification.'

Happy suggestion

Software diversification.

Software diversification

Suggestions

  • diversification.

al.

replace 'al.' by 'was'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • was

et

replace 'et' by 'It'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • It

Stiévenart

replace 'Stiévenart' by 'al.'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • al.

and

replace 'and' by 'et'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • et

al.

replace 'al.' by 'Stiévenart'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • Stiévenart

et

replace 'et' by 'al.'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • al.

Lehmann

replace 'Lehmann' by 'et'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • et

by

replace 'by' by 'Lehmann'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • Lehmann

studies

replace 'studies' by 'by'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • by

example,

replace 'example,' by 'studies'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • studies

For

replace 'For' by 'example,'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • example,

vulnerable.

replace 'vulnerable.' by 'For'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • For

inherently

replace 'inherently' by 'vulnerable.'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • vulnerable.

be

replace 'be' by 'inherently'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • inherently

may

replace 'may' by 'be'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • be

themselves

replace 'themselves' by 'may'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • may

binaries

replace 'binaries' by 'themselves'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • themselves

the

replace 'the' by 'binaries'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • binaries

Finally,

replace 'Finally,' by 'the'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • the

[18].

replace '[18].' by 'Finally,'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • Finally,

browsers

replace 'browsers' by '[18].'

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Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • [18].

in

replace 'in' by 'browsers'

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Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • browsers

binaries

replace 'binaries' by 'in'

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Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • in

on

replace 'on' by 'WebAssembly'

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Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • WebAssembly

attacks

replace 'attacks' by 'on'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • on

side-channel

replace 'side-channel' by 'attacks'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • attacks

contention

replace 'contention' by 'side-channel'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • side-channel

port

replace 'port' by 'contention'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • contention

for

replace 'for' by 'port'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • port

possibility

replace 'possibility' by 'for'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • for

the

replace 'the' by 'possibility'

Happy suggestion

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • possibility

disclosed

replace 'disclosed' by 'The'

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Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].

Suggestions

  • The

challenges

replace 'challenges' by 'challenges.'

Happy suggestion

Open challenges.

Open challenges

Suggestions

  • challenges.

variants

replace 'variants' by 'variants.'

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Automatic generation of software variants.

Automatic generation of software variants

Suggestions

  • variants.

instr1i

Possible typo detected.

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The concept of automatic software variants starts with Randell’s 1975 work [79], which put forth the notion of artificial fault-tolerant instruction blocks. Artificial Software Diversification, as proposed by Cohen and Forrest in the 1990s [33, 34], gets its development through rewriting strategies. These strategies consist of sets of rewriting rules for modifying software components to create functionally equivalent, yet distinct, programs. Rewriting strategies typically take the form of tuples: instr1i => (instr2, instr3,...), where instr represents the original code and (instr2, instr3,...) denotes the functionally equivalent code.

Suggestions

  • instr

Reordering:

replace 'Reordering:' by 'Reordering'

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Instruction Reordering This strategy reorders instructions in a program. For example, variable declarations may change if compilers reorder them in the symbol tables. This prevents static examination and analysis of parameters and alters memory locations. In this area, Bhatkar et al. [83, 84] proposed the random permutation of variable and routine order for ELF binaries. Such strategies are not implemented for WebAssembly to the best of our knowledge.

Instruction Reordering: This strategy reorders instructions in a program. For example, variable declarations may change if compilers reorder them in the symbol tables. This prevents static examination and analysis of parameters and alters memory locations. In this area, Bhatkar et al. [83, 84] proposed the random permutation of variable and routine order for ELF binaries. Such strategies are not implemented for WebAssembly to the best of our knowledge.

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  • Reordering

strategy:

delete 'strategy:' by ''

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The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.

Rewriting strategy: The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.

Suggestions

Rewriting

delete 'Rewriting' by ''

Happy suggestion

The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.

Rewriting strategy: The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.

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implement

replace 'implement' by 'Implement'

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ISA Randomization and Simulation: This strategy involves using a key to cipher the original program binary into another encoded binary. Once encoded, the program can only be decoded at the target client, or it can be interpreted in the encoded form using a custom virtual machine implementation. This technique is strong against attacks involving code inspection. Kc et al. [92], and Barrantes et al. [93] proposed seminal works on instruction-set randomization to create a unique mapping between artificial CPU instructions and real ones. On the same topic, Chew and Song [94] target operating system randomization. They randomize the interface between the operating system and the user applications. Couroussé et al. [95] Implement an assembly-like DSL to generate equivalent code at runtime in order to increase protection against side-channel attacks. Their technique generates a different program during execution using an interpreter for their DSL. Generally, [SA randomization and simulation usually faces a performance penalty, especially for WebAssembly, due to the decoding process as shown in WASMixer evaluation [96].

ISA Randomization and Simulation: This strategy involves using a key to cipher the original program binary into another encoded binary. Once encoded, the program can only be decoded at the target client, or it can be interpreted in the encoded form using a custom virtual machine implementation. This technique is strong against attacks involving code inspection. Kc et al. [92], and Barrantes et al. [93] proposed seminal works on instruction-set randomization to create a unique mapping between artificial CPU instructions and real ones. On the same topic, Chew and Song [94] target operating system randomization. They randomize the interface between the operating system and the user applications. Couroussé et al. [95] implement an assembly-like DSL to generate equivalent code at runtime in order to increase protection against side-channel attacks. Their technique generates a different program during execution using an interpreter for their DSL. Generally, [SA randomization and simulation usually faces a performance penalty, especially for WebAssembly, due to the decoding process as shown in WASMixer evaluation [96].

Suggestions

  • Implement

[

Unpaired symbol: ‘]’ seems to be missing

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ISA Randomization and Simulation: This strategy involves using a key to cipher the original program binary into another encoded binary. Once encoded, the program can only be decoded at the target client, or it can be interpreted in the encoded form using a custom virtual machine implementation. This technique is strong against attacks involving code inspection. Kc et al. [92], and Barrantes et al. [93] proposed seminal works on instruction-set randomization to create a unique mapping between artificial CPU instructions and real ones. On the same topic, Chew and Song [94] target operating system randomization. They randomize the interface between the operating system and the user applications. Couroussé et al. [95] implement an assembly-like DSL to generate equivalent code at runtime in order to increase protection against side-channel attacks. Their technique generates a different program during execution using an interpreter for their DSL. Generally, [SA randomization and simulation usually faces a performance penalty, especially for WebAssembly, due to the decoding process as shown in WASMixer evaluation [96].

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Yet,

delete 'Yet,' by ''

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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functions.

delete 'functions.' by ''

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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complementary

delete 'complementary' by ''

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

to

replace 'to' by 'the'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • the

calls

replace 'calls' by 'Yet,'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • Yet,

by

replace 'by' by 'functions.'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • functions.

replaced

replace 'replaced' by 'WebAssembly'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • WebAssembly

is

replace 'is' by 'complementary'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • complementary

code

replace 'code' by 'to'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • to

the

replace 'the' by 'calls'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • calls

of

replace 'of' by 'by'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • by

part

replace 'part' by 'replaced'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • replaced

which

replace 'which' by 'is'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • is

in

replace 'in' by 'code'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • code

JavaScript

replace 'JavaScript' by 'the'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • the

for

replace 'for' by 'of'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • of

wobfuscator,

replace 'wobfuscator,' by 'part'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • part

technique,

replace 'technique,' by 'which'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • which

obfuscation

replace 'obfuscation' by 'in'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • in

an

replace 'an' by 'JavaScript'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • JavaScript

proposed

replace 'proposed' by 'for'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • for

[87]

replace '[87]' by 'wobfuscator,'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • wobfuscator,

al.

replace 'al.' by 'technique,'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • technique,

et

replace 'et' by 'obfuscation'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • obfuscation

Romano

replace 'Romano' by 'an'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • an

WebAssembly,

replace 'WebAssembly,' by 'proposed'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • proposed

of

replace 'of' by '[87]'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • [87]

context

replace 'context' by 'al.'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • al.

the

replace 'the' by 'et'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • et

In

replace 'In' by 'Romano'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • Romano

programs.

replace 'programs.' by 'WebAssembly,'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • WebAssembly,

insert '' by 'context'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • context

engineering

replace 'engineering' by 'the'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • the

reverse

replace 'reverse' by 'In'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • In

static

replace 'static' by 'programs.'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • programs.

tackle

replace 'tackle' by 'of'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • of

to

replace 'to' by 'engineering'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • engineering

aim

replace 'aim' by 'reverse'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • reverse

strategies

replace 'strategies' by 'static'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • static

both

replace 'both' by 'tackle'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • tackle

Yet,

replace 'Yet,' by 'to'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • to

[97].

replace '[97].' by 'aim'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • aim

client

replace 'client' by 'strategies'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • strategies

any

replace 'any' by 'both'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • both

in

replace 'in' by 'Yet,'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • Yet,

is

replace 'is' by '[97].'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • [97].

as

replace 'as' by 'client'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • client

executes

replace 'executes' by 'any'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • any

latter

replace 'latter' by 'in'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • in

the

replace 'the' by 'is'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • is

while

replace 'while' by 'as'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • as

key

replace 'key' by 'executes'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • executes

encoding

replace 'encoding' by 'latter'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • latter

know

delete 'know' by ''

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

to

replace 'to' by 'while'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • while

target

replace 'target' by 'key'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • key

final

replace 'final' by 'encoding'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • encoding

insert '' by 'know'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • know

insert '' by 'to'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • to

requires

replace 'requires' by 'target'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • target

former

replace 'former' by 'final'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • final

insert '' by 'requires'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • requires

that

replace 'that' by 'latter'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • latter

is

replace 'is' by 'the'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • the

code

replace 'code' by 'that'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • that

obfuscating

replace 'obfuscating' by 'is'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • is

and

replace 'and' by 'code'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • code

encoding

replace 'encoding' by 'obfuscating'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • obfuscating

between

replace 'between' by 'and'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • and

difference

replace 'difference' by 'encoding'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • encoding

main

replace 'main' by 'between'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • between

The

replace 'The' by 'difference'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • difference

randomization.

replace 'randomization.' by 'main'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • main

[S'A

replace '[S'A' by 'The'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • The

of

replace 'of' by 'randomization.'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • randomization.

simplification

replace 'simplification' by '[S'A'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • [S'A

a

replace 'a' by 'of'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • of

as

replace 'as' by 'simplification'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • simplification

seen

replace 'seen' by 'a'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • a

be

replace 'be' by 'as'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

Suggestions

  • as

can

replace 'can' by 'seen'

Happy suggestion

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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obfuscation

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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obfuscation:

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.

Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.

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Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking

Equivalence Checking

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Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking

Equivalence Checking

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deployment

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Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment.

Variants deployment

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Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment.

Variants deployment

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Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment.

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verification:

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In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].

Formal verification: In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].

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Formal

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In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].

Formal verification: In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].

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tests:

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The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.

Checking modulo tests: The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.

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modulo

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The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.

Checking modulo tests: The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.

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Checking

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The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.

Checking modulo tests: The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.

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propose

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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Aga

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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combinations.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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flag

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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compiler

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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different

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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using

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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by

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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program

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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a

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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of

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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variants

replace 'variants' by 'program'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • program

multiple

replace 'multiple' by 'a'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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generates

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • of

Cornucopia

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • variants

[111].

replace '[111].' by 'multiple'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • multiple

Cornucopia

replace 'Cornucopia' by 'generates'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • generates

propose

replace 'propose' by 'Cornucopia'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • Cornucopia

al.,

replace 'al.,' by '[111].'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • [111].

et

replace 'et' by 'Cornucopia'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • Cornucopia

Singhal

replace 'Singhal' by 'propose'

Happy suggestion

In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • propose

Similarly,

replace 'Similarly,' by 'al.'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • al.

multistacks.

replace 'multistacks.' by 'et'

Happy suggestion

In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • et

termed

replace 'termed' by 'Singhal'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • Singhal

parts,

replace 'parts,' by 'Similarly,'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • Similarly,

component

replace 'component' by 'multistacks.'

Happy suggestion

In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • multistacks.

several

replace 'several' by 'termed'

Happy suggestion

In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • termed

into

replace 'into' by 'parts,'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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conventional

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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a

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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partition

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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to

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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4.1

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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GCC

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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of

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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version

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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a

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • GCC

adapt

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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They

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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process.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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compilation

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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the

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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varying

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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generate

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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to

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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custom

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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a

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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employing

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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suggest

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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[110]

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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binaries

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • affect

vulnerable

replace 'vulnerable' by 'only'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • only

as

replace 'as' by 'can'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • can

immunity,

replace 'immunity,' by 'binaries'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • binaries

herd

replace 'herd' by 'vulnerable'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • vulnerable

Randomization

delete 'Randomization' by ''

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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in

replace 'in' by 'immunity,'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • immunity,

pool

replace 'pool' by 'herd'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • herd

variant

replace 'variant' by 'as'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • as

the

replace 'the' by 'Randomization'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • Randomization

[73]

replace '[73]' by 'pool'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • pool

al.

replace 'al.' by 'variant'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • variant

et

replace 'et' by 'the'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Suggestions

  • the

Jackson

replace 'Jackson' by 'define'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • define

deployment.

replace 'deployment.' by '[73]'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • [73]

each

replace 'each' by 'al.'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • al.

during

replace 'during' by 'et'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • et

client

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • Jackson

the

replace 'the' by 'deployment.'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • deployment.

a

replace 'a' by 'each'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • each

to

replace 'to' by 'during'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • during

assigned

replace 'assigned' by 'client'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • client

is

replace 'is' by 'a'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • a

pool),

replace 'pool),' by 'to'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • to

variant

replace 'variant' by 'assigned'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • assigned

program’s

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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the

replace 'the' by 'pool),'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • pool),

as

replace 'as' by 'variant'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • variant

to

replace 'to' by 'program’s'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • program’s

(referred

replace '(referred' by 'the'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • the

variants

replace 'variants' by 'as'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • as

collection

replace 'collection' by '(referred'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • (referred

a

replace 'a' by 'variants'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • variants

from

replace 'from' by 'of'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • of

chosen

replace 'chosen' by 'collection'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • collection

randomly

replace 'randomly' by 'a'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • a

program,

replace 'program,' by 'from'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • from

a

replace 'a' by 'selected'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • selected

setup,

replace 'setup,' by 'randomly'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • randomly

this

replace 'this' by 'program,'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • program,

In

replace 'In' by 'a'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • a

clients.

replace 'clients.' by 'setup,'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • setup,

different

replace 'different' by 'this'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • this

to

replace 'to' by 'In'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • In

variants

replace 'variants' by 'clients.'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • clients.

present

delete 'present' by ''

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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ability

delete 'ability' by ''

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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program’s

replace 'program’s' by 'variants'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • variants

a

replace 'a' by 'different'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • different

denotes

replace 'denotes' by 'present'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • present

Randomization

replace 'Randomization' by 'to'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • to

term

replace 'term' by 'ability'

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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  • ability

the

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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memory

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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detection

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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the

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • [114].

for

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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allowing

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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direction,

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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opposite

replace 'opposite' by 'detection'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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in

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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grows

replace 'grows' by 'allowing'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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that

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • direction,

stack

replace 'stack' by 'opposite'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • opposite

a

replace 'a' by 'the'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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have

replace 'have' by 'in'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • in

variants

replace 'variants' by 'grows'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • grows

These

replace 'These' by 'that'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • that

variants.

replace 'variants.' by 'stack'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Intel

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • a

32-bit

replace '32-bit' by 'have'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • have

generate

replace 'generate' by 'variants'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • variants

to

replace 'to' by 'These'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • These

library

replace 'library' by 'variants.'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • variants.

standard

replace 'standard' by 'Intel'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • Intel

a

replace 'a' by '32-bit'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • 32-bit

modify

replace 'modify' by 'generate'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • generate

al.

replace 'al.' by 'to'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • to

et

replace 'et' by 'library'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • library

Salamat

replace 'Salamat' by 'standard'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • standard

time,

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • et

[113].

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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layouts

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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memory

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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and

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • [113].

introducing

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • memory

in

replace 'in' by 'randomized'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • randomized

variants

replace 'variants' by 'and'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • and

two

replace 'two' by 'non-overlapping'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • non-overlapping

executing

replace 'executing' by 'introducing'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • introducing

of

replace 'of' by 'parallel,'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • parallel,

concept

replace 'concept' by 'in'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • in

the

replace 'the' by 'variants'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • variants

extend

replace 'extend' by 'two'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • two

al.

replace 'al.' by 'executing'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • executing

et

replace 'et' by 'of'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • of

Bruschi

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • concept

[83].

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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paths

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • Extended

execution

replace 'execution' by 'al.'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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randomize

replace 'randomize' by 'et'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • et

to

replace 'to' by 'Bruschi'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • Bruschi

program

replace 'program' by '[83].'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • [83].

single

replace 'single' by 'paths'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • paths

a

replace 'a' by 'execution'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • execution

as

replace 'as' by 'randomize'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • randomize

or

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • to

inconsistencies,

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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for

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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check

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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to

replace 'to' by 'as'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • as

parallel

replace 'parallel' by 'or'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • or

in

replace 'in' by 'inconsistencies,'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • inconsistencies,

execute

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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either

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • check

can

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • to

variants

replace 'variants' by 'parallel'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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embedded

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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These

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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program

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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its

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Then,

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • binary

a

replace 'a' by 'multivariant'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • multivariant

to

replace 'to' by 'the'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • the

deployed

replace 'deployed' by 'Then,'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • Then,

randomly

replace 'randomly' by 'client.'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • client.

is

replace 'is' by 'a'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • a

binary

replace 'binary' by 'to'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • to

Each

replace 'Each' by 'randomly'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • randomly

[112].

replace '[112].' by 'is'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • is

a

delete 'a' by ''

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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as

replace 'as' by 'Each'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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known

replace 'known' by '[112].'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • [112].

binary,

replace 'binary,' by 'binary'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • binary

single

replace 'single' by 'multivariant'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • multivariant

insert '' by 'as'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • as

insert '' by 'known'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • known

into

replace 'into' by 'binary,'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • binary,

composed

replace 'composed' by 'single'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • single

are

replace 'are' by 'a'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • a

variants

replace 'variants' by 'into'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Suggestions

  • into

program

replace 'program' by 'composed'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • composed

Multiple

replace 'Multiple' by 'are'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • are

(MVE):

replace '(MVE):' by 'variants'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • variants

Execution

replace 'Execution' by 'program'

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Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.

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  • program

Diversification

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Measuring Software Diversification.

Measuring Software Diversification

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that,

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Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an example. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. Use a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.

Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an instance. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. used a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that, dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.

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used

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Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an example. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. Use a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.

Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an instance. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. used a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that, dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.

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instance.

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Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an example. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. Use a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.

Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an instance. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. used a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that, dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.

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exist

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In the context of WebAssembly, there are no explicit works on Software Diversification. Consequently, previous metrics have not been directly applied to measure diversification in WebAssembly binaries. However, in other domains, such as the analysis of WebAssembly binaries, several studies have employed static metrics. For example, VeriWasm quantifies attack-based patterns, stating that a WebAssembly binary is more secure with a lower pattern count [128]. This metric might potentially serve as a guide during variant generation. In the field of malware detection, MINOS [28] proposes transforming WebAssembly binaries into grayscale images. They then employ convolutional neural networks to identify malware, where an increased similarity to a malware image increases the probability of the binary being malware. Regarding the dynamic comparisons, Wang et al.’s study [29] profiles WebAssembly instructions during runtime to identify malicious behavior.

In the context of WebAssembly, there exist no explicit works on Software Diversification. Consequently, previous metrics have not been directly applied to measure diversification in WebAssembly binaries. However, in other domains, such as the analysis of WebAssembly binaries, several studies have employed static metrics. For example, VeriWasm quantifies attack-based patterns, stating that a WebAssembly binary is more secure with a lower pattern count [128]. This metric might potentially serve as a guide during variant generation. In the field of malware detection, MINOS [28] proposes transforming WebAssembly binaries into grayscale images. They then employ convolutional neural networks to identify malware, where an increased similarity to a malware image increases the probability of the binary being malware. Regarding the dynamic comparisons, Wang et al.’s study [29] profiles WebAssembly instructions during runtime to identify malicious behavior.

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propose

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Malicious actors may employ previously discussed diversification strategies to evade detection [131]. For instance, in the Web context, Weihang et al. Propose to randomly transform HTML elements of web pages to evade advertisement blockers [132]. Over time, evasion techniques have evolved in both complexity and sophistication [133]. Chua et al. [134], for instance, suggested a framework for automatically obfuscating the source code of Android applications using method overloading, opaque predicates, try-catch, and switch statement obfuscation, resulting in multiple versions of identical malware. Moreover, machine learning approaches have been used to develop evasive malware [135], drawing on a corpus of pre-existing malware [124]. These methods aim to thwart static malware detectors, yet, more advanced techniques focus on evading dynamic detection mostly by employing throttling [136, 137].

Malicious actors may employ previously discussed diversification strategies to evade detection [131]. For instance, in the Web context, Weihang et al. propose to randomly transform HTML elements of web pages to evade advertisement blockers [132]. Over time, evasion techniques have evolved in both complexity and sophistication [133]. Chua et al. [134], for instance, suggested a framework for automatically obfuscating the source code of Android applications using method overloading, opaque predicates, try-catch, and switch statement obfuscation, resulting in multiple versions of identical malware. Moreover, machine learning approaches have been used to develop evasive malware [135], drawing on a corpus of pre-existing malware [124]. These methods aim to thwart static malware detectors, yet, more advanced techniques focus on evading dynamic detection mostly by employing throttling [136, 137].

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  • Propose

Diversification:

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Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.

Offensive Diversification: Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.

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Offensive

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Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.

Offensive Diversification: Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.

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Diversification

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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Software

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • diversification

Defensive

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • software

hand,

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • defensive

one

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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the

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • one

On

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • the

Diversification.

replace 'Diversification.' by 'On'

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • On

Offensive

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • offensive.

Defensive

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Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].

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  • defensive

diversification

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Offensive or defensive assessment of diversification.

Offensive or Defensive assessment of diversification

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  • diversification.

Defensive

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Offensive or defensive assessment of diversification.

Offensive or Defensive assessment of diversification

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  • defensive

Diversification

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Open challenges for Software Diversification.

Open challenges for Software Diversification

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  • Diversification.

«¢

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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1

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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'

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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j

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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i

delete 'i' by ''

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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based

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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|

delete '|' by ''

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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Compiler

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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°

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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{

delete '{' by ''

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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replace '' by '/>'

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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  • />

fooo

replace 'fooo' by 'Foo'

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Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re

fooo |. ) Wasm , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢

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  • Foo

insert '' by 'code.'

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Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.

source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original

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  • code.

insert '' by 'source'

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Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.

source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original

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  • source

i

replace 'i' by 'The'

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Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.

source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original

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  • The

source

replace 'source' by 'Source'

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Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.

source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original

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  • Source

source

This sentence does not start with an uppercase letter.

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source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original

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  • Source

Wheeler

replace 'Wheeler' by 'Wheeler.'

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— David Wheeler.

— David Wheeler

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  • Wheeler.

FOR

delete 'FOR' by ''

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AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS

AUTOMATIC SOFTWARE DIVERSIFICATION FOR

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DIVERSIFICATION

replace 'DIVERSIFICATION' by 'DIVERS'

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AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS

AUTOMATIC SOFTWARE DIVERSIFICATION FOR

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  • DIVERS

insert '' by 'FOR'

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AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS

AUTOMATIC SOFTWARE DIVERSIFICATION FOR

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  • FOR

insert '' by 'DIVERSIFICATION'

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AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS

AUTOMATIC SOFTWARE DIVERSIFICATION FOR

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  • DIVERSIFICATION

insert '' by 'SOFTWARE'

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AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS

AUTOMATIC SOFTWARE DIVERSIFICATION FOR

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  • SOFTWARE

insert '' by 'AUTOMATIC'

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AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS

AUTOMATIC SOFTWARE DIVERSIFICATION FOR

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  • AUTOMATIC

synthesis

replace 'synthesis' by 'synthesis.'

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Enumerative synthesis.

Enumerative synthesis

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  • synthesis.

insert '' by 'Functions'

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Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions

Function

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  • Functions

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Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions

Function

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  • Functions:

Function

replace 'Function' by 'Functions:'

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Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions

Function

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  • Functions:

transformations

replace 'transformations' by 'transformations.'

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CROW is carefully designed to boost the generation of variants as much as possible. First, we disable the majority of the pruning strategies. Instead of preventing the generation of commutative operations during the search, CROW still uses such transformation as a strategy to generate program variants. Second, CROW applies code transformations independently. For instance, if a suitable replacement is identified that can be applied at N different locations in the original program, CROW will generate 2" distinct program variants, i.e., the power set of applying the transformation or not to each location. This approach leads to a combinatorial explosion in the number of available program variants, especially as the number of possible replacements increases. Third, we remove all built-in optimizations in the LLVM backend that could reverse Wasm variants, i.e., we disable all optimizations in the Wasm backend that could reverse the CROW transformations.

CROW is carefully designed to boost the generation of variants as much as possible. First, we disable the majority of the pruning strategies. Instead of preventing the generation of commutative operations during the search, CROW still uses such transformation as a strategy to generate program variants. Second, CROW applies code transformations independently. For instance, if a suitable replacement is identified that can be applied at N different locations in the original program, CROW will generate 2" distinct program variants, i.e., the power set of applying the transformation or not to each location. This approach leads to a combinatorial explosion in the number of available program variants, especially as the number of possible replacements increases. Third, we remove all built-in optimizations in the LLVM backend that could reverse Wasm variants, i.e., we disable all optimizations in the Wasm backend that could reverse the CROW transformations

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  • transformations.

{

delete '{' by ''

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Int babbage() int current = 25264;

int babbage() { int current = 25264;

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int

replace 'int' by 'Int'

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Int babbage() int current = 25264;

int babbage() { int current = 25264;

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  • Int

;}

delete ';}' by ''

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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0

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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3

delete '3' by ''

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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>

delete '>' by ''

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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current)

replace 'current)' by ';'

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • ;

%d\n",

replace '%d\n",' by '0'

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 0

is

replace 'is' by 'return'

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • return

number

replace 'number' by '3'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 3

("The

replace '("The' by '>'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • >

printf

replace 'printf' by 'current)'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • current)

25264;

replace '25264;' by '%dn",'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • %dn",

=

replace '=' by 'is'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • is

current

replace 'current' by 'number'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • number

int

replace 'int' by '("The'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • ("The

{

replace '{' by 'printf'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • printf

babbage()

replace 'babbage()' by '25264;'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 25264;

int

replace 'int' by '='

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • =

[40].

replace '[40].' by 'current'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • current

from

replace 'from' by 'int'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • int

Taken

replace 'Taken' by 'babbage()'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • babbage()

problem.

replace 'problem.' by 'Int'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • Int

Babbage

replace 'Babbage' by '[40].'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • [40].

3.1:

replace '3.1:' by 'from'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • from

Listing

replace 'Listing' by 'Taken'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • Taken

;

replace ';' by 'problem.'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • problem.

0

replace '0' by 'Babbage'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • Babbage

current);

replace 'current);' by 'Listing'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • Listing

<

replace '<' by '0;'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 0;

7%d\n",

replace '7%d\n",' by 'return'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • return

is

replace 'is' by 'current);'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • current);

number

replace 'number' by '7%dn",'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 7%dn",

("The

replace '("The' by 'is'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • is

printf

replace 'printf' by 'number'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • number

+

replace '+' by '("The'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • ("The

current++;

replace 'current++;' by 'printf'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • printf

{

replace '{' by '+'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • +

269696)

replace '269696)' by 'current++;'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • current++;

!=

replace '!=' by '269696)'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 269696)

1000000

replace '1000000' by '!='

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • !=

<—

replace '<—' by '1000000'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • 1000000

%

replace '%' by '—'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

((square=current*current)

replace '((square=current*current)' by '%'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

Suggestions

  • %

while

replace 'while' by '((square=current*current)'

Happy suggestion

RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • ((square=current*current)

square;

replace 'square;' by 'while'

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • while

0,

replace '0,' by 'square;'

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • square;

=

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • 0,

current

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • =

int

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • current

{}

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RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].

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  • int

inferring

replace 'inferring' by 'inferring.'

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Constant inferring.

Constant inferring

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  • inferring.

insert '' by 'NO'

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NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO

NO

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)

Unpaired symbol: ‘(’ seems to be missing

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1 2 3 ing £Cint =) 4 return 2 * x + x; a Listing 3.3: C function that calculates the quantity 2x + x.

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runtime?.

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CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached! Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?

CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached!. Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?.

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reached!.

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CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached! Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?

CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached!. Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?.

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?Notice

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In theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. Note that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.

lIn theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. ?Notice that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.

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lIn

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In theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. Note that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.

lIn theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. ?Notice that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.

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Notice

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lIn theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. ?Notice that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.

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132.mul

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func $f (param i32) (result i32) local.get 0 1i32.const 3 132.mules.

func $f (param i32) (result i32) local.get 0 1i32.const 3 132.mul

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  • 132.mules.

get

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func $f (param i32) (result i32) local.get 0 1i32.const 3 132.mul

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  • get

i32.add

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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1i32.add

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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  • i32.add

insert '' by '0'

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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insert '' by 'local.get'

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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  • 1i32.add

i32)

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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  • 0

(result

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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insert '' by 'i32)'

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32

func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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  • i32)

get

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func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add

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  • get

i32.add

delete 'i32.add' by ''

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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0

replace '0' by 'i32.'

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • i32.

local.get

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • i32.add

132.shl1

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • 0

1

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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132.const

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • 132.shl1

0

replace '0' by '1'

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • 1

local.get

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • 132.const

i32)

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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(result

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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insert '' by 'i32)'

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • i32)

get

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func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add

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  • get

i32.add

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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0

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • i32.

local.get

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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i132.mul

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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2

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • local.get

1i32.const

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • i132.mul

0

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • 2

local.get

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • 1i32.const

i32)

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • 0

(result

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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insert '' by 'i32)'

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.

func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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  • i32)

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func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add

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3:i32

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42 13 shl nsw i32 %0, 1:132 mul nsw 40, 3:i32.

42 13 shl nsw i32 %0, 1:132 mul nsw 40, 3:i32

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  • 3:i32.

43

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Add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43.

add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43

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  • 43.

add

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Add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43.

add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43

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  • Add

%0,%2

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h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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%0,2

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h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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insert '' by 'add'

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h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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  • add

insert '' by '%0,%2'

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h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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  • %0,%2

insert '' by 'i32'

Happy suggestion

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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  • i32

insert '' by 'nsw'

Happy suggestion

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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  • nsw

insert '' by '%0,2'

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h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %

h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2

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  • %0,2

insert '' by 'nsw.'

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42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32 mul nsw.

42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32

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  • nsw.

insert '' by 'mul'

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42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32 mul nsw.

42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32

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  • mul

1:i32

delete '1:i32' by ''

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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%0,

replace '%0,' by '1:i32.'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • 1:i32.

i32

replace 'i32' by '%0,'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • %0,

nsw

replace 'nsw' by 'i32'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • i32

shl

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • nsw

=

replace '=' by 'shl'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • shl

72

replace '72' by '='

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • =

%1}

replace '%1}' by '72'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • 72

i132

replace 'i132' by '%1'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • %1

ret

replace 'ret' by 'i132'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • i132

10)

replace '10)' by 'ret'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • ret

@f(i32

replace '@f(i32' by '10)'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • 10)

i32

replace 'i32' by '@f(i32'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • @f(i32

call

replace 'call' by 'i32'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • i32

tail

replace 'tail' by 'call'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • call

=

replace '=' by 'tail'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • tail

%1

replace '%1' by '='

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • =

{

replace '{' by '%1'

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define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.

define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • %1

{

Don’t put a space after the opening parenthesis.

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define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32

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  • {

3:132

replace '3:132' by '3:132.'

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72 = add nsw i32 %0,%0 %3 = mul nsw 40, 3:132.

72 = add nsw i32 %0,%0 %3 = mul nsw 40, 3:132

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  • 3:132.

43

replace '43' by '43.'

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Ret i32 43.

ret i32 43

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  • 43.

ret

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Ret i32 43.

ret i32 43

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  • Ret

for_code1___code_2

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define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.

define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2

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for

delete 'for' by ''

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define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.

define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2

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candidates

replace 'candidates' by 'for_code1___code_2__.'

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define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.

define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2

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  • for_code1___code_2__.

Replacement

replace 'Replacement' by 'for'

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define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.

define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2

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  • for

insert '' by 'candidates'

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define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.

define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2

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  • candidates

{

replace '{' by 'Replacement'

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define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.

define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2

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  • Replacement

3.6:

delete '3.6:' by ''

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Wasm program variants generated from program Listing 3.8.

Listing 3.6: Wasm program variants generated from program Listing 3.8.

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Listing

delete 'Listing' by ''

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Wasm program variants generated from program Listing 3.8.

Listing 3.6: Wasm program variants generated from program Listing 3.8.

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respectively

replace 'respectively' by 'respectively,'

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CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

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  • respectively,

code

replace 'code' by 'code,'

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CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

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  • code,

shows.

replace 'shows.' by 'show.'

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CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

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  • show.

CROW,

replace 'CROW,' by 'CROW'

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CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.

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  • CROW

https://github.com/ASSERT-KTH/slumps

replace 'https://github.com/ASSERT-KTH/slumps' by 'https://github.com/ASSERT-KTH/slumps.'

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Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assemble them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps.

Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assembles them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps

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  • https://github.com/ASSERT-KTH/slumps.

assembles

replace 'assembles' by 'assemble'

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Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assemble them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps.

Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assembles them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps

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  • assemble

graph

replace 'graph' by 'graphs.'

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Multivariant call graphs.

Multivariant call graph

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  • graphs.

the

replace 'the' by 'The'

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As illustrated in Figure 3.3, MEWE takes the LLVM IR variants generated by CROW’s diversifier. It then merges LLVM IR variants into a Wasm multivariant. In the figure, we highlight the two components of MEWE, Multivariant Generation and the Mizer. In the Multivariant Generation process, MEWE gathers the LLVM IR variants created by CROW. The Mixer component, on the other hand, links the multivariant binary and creates a new entrypoint for the binary. Creating a new entrypoint is needed in case the output of CROW are variants of the original entrypoint, e.g. The main function. Concretely, it wraps the dispatcher for the entrypoint variants as a new function for the final Wasm binary and is declared as the application entrypoint. The random generator is needed to perform the execution-path randomization. For the random generator, we rely on WASI’s specification [41] for the random behavior of the dispatchers. However, its exact implementation is dependent on the host engine on which the binary is executed. Finally, using the same custom Wasm LLVM backend as CROW, we generate a standalone multivariant Wasm binary. Once generated, the multivariant Wasm binary can be deployed to any Wasm engine.

As illustrated in Figure 3.3, MEWE takes the LLVM IR variants generated by CROW’s diversifier. It then merges LLVM IR variants into a Wasm multivariant. In the figure, we highlight the two components of MEWE, Multivariant Generation and the Mizer. In the Multivariant Generation process, MEWE gathers the LLVM IR variants created by CROW. The Mixer component, on the other hand, links the multivariant binary and creates a new entrypoint for the binary. Creating a new entrypoint is needed in case the output of CROW are variants of the original entrypoint, e.g. the main function. Concretely, it wraps the dispatcher for the entrypoint variants as a new function for the final Wasm binary and is declared as the application entrypoint. The random generator is needed to perform the execution-path randomization. For the random generator, we rely on WASI’s specification [41] for the random behavior of the dispatchers. However, its exact implementation is dependent on the host engine on which the binary is executed. Finally, using the same custom Wasm LLVM backend as CROW, we generate a standalone multivariant Wasm binary. Once generated, the multivariant Wasm binary can be deployed to any Wasm engine.

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  • The

at

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This section describes MEWE [37], our second technical contribution. MEWE synthesizes diversified function variants by using CROW. It then provides execution-path randomization in a Multivariant Execution (MVE) [83]. Execution path randomization is a technique that randomizes the execution path of a program at runtime, i.e. At each invocation of a function, a different variant is executed. MEWE generates application-level multivariant binaries without changing the operating system or Wasm runtime. It creates an MVE by intermixing functions for which CROW generates variants, as illustrated by the green square in Figure 3.1. MEWE inlines function variants when appropriate, resulting in call stack diversification at runtime.

This section describes MEWE [37], our second technical contribution. MEWE synthesizes diversified function variants by using CROW. It then provides execution-path randomization in a Multivariant Execution (MVE) [83]. Execution path randomization is a technique that randomizes the execution path of a program at runtime, i.e. at each invocation of a function, a different variant is executed. MEWE generates application-level multivariant binaries without changing the operating system or Wasm runtime. It creates an MVE by intermixing functions for which CROW generates variants, as illustrated by the green square in Figure 3.1. MEWE inlines function variants when appropriate, resulting in call stack diversification at runtime.

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insert '' by 'the'

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Figure 3.3: Overview of the MEWE workflow. It takes as input an LLVM binary. It first generates a set of functionally equivalent variants for each function in the binary using CROW. Then, MEWE generates an LLVM multivariant binary composed of all the function variants. Then, the Mixer includes the behavior in charge of selecting a variant when a function is invoked. Finally, the MEWE mixer composes the LLVM multivariant binary with a random number generation library and tampers the original application entrypoint. The final process produces a Wasm multivariant binary ready to be deployed.

Figure 3.3: Overview of MEWE workflow. It takes as input an LLVM binary. It first generates a set of functionally equivalent variants for each function in the binary using CROW. Then, MEWE generates an LLVM multivariant binary composed of all the function variants. Then, the Mixer includes the behavior in charge of selecting a variant when a function is invoked. Finally, the MEWE mixer composes the LLVM multivariant binary with a random number generation library and tampers the original application entrypoint. The final process produces a Wasm multivariant binary ready to be deployed.

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Le

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Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I free function, functiony.

Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le

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functiony

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Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I free function, functiony.

Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le

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  • functiony.

freee

replace 'freee' by 'free'

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Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I free function, functiony.

Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le

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  • free

]

Unpaired symbol: ‘[’ seems to be missing

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Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le

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binary

replace 'binary' by 'binary.'

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Exemplifying a Multivariant binary.

Exemplifying a Multivariant binary

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  • binary.

insert '' by 'it.'

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Then there is the question of whether or not there is a need for it.

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  • it.

insert '' by 'for'

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Then there is the question of whether or not there is a need for it.

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  • for

insert '' by 'need'

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Then there is the question of whether or not there is a need for it.

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insert '' by 'a'

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Then there is the question of whether or not there is a need for it.

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insert '' by 'is'

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Then there is the question of whether or not there is a need for it.

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insert '' by 'there'

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Then there is the question of whether or not there is a need for it.

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insert '' by 'not'

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Then there is the question of whether or not there is a need for it.

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insert '' by 'or'

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Then there is the question of whether or not there is a need for it.

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Then there is the question of whether or not there is a need for it.

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insert '' by 'of'

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Then there is the question of whether or not there is a need for it.

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Then there is the question of whether or not there is a need for it.

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insert '' by 'the'

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Then there is the question of whether or not there is a need for it.

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  • the

insert '' by 'Then'

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Then there is the question of whether or not there is a need for it.

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  • Then

insert '' by 'Variant'

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e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant

e Variant

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insert '' by 'e'

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e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant

e Variant

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Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original

© Original

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©

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Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original

© Original

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3.7:

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Listing 3.7. Dispatcher function embedded in the multivariant binary of the original function in the rightmost green node in Figure 3.4. The code is commented on for the sake of understanding.

Listing 3.7: Dispatcher function embedded in the multivariant binary of the original function in the rightmost green node in Figure 3.4. The code is commented on for the sake of understanding.

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%4

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define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

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i32

delete 'i32' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

ret

delete 'ret' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

(%0)

delete '(%0)' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

@foo_original

delete '@foo_original' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

call

delete 'call' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

=

delete '=' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

end:

delete 'end:' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

%3

delete '%3' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

inlined>

delete 'inlined>' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

foo_44_

delete 'foo_44_' by ''

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

delete '

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

=

replace '=' by 'body'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • body

%3

replace '%3' by '='

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • =

case_44_:

replace 'case_44_:' by '%3'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • %3

%2

replace '%2' by 'case_44_:'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • case_44_:

i32

replace 'i32' by '%2'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • %2

ret

replace 'ret' by 'i32'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • i32

@f00_43_(%0)

replace '@f00_43_(%0)' by 'ret'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • ret

i32

replace 'i32' by '@f00_43_(%0)'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • @f00_43_(%0)

call

replace 'call' by 'i32'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • i32

=

replace '=' by 'call'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • call

42

replace '42' by '='

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • =

case_43_:

replace 'case_43_:' by '42'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • 42

%case_44_]

replace '%case_44_]' by 'case_43_:'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • case_43_:

label

replace 'label' by '%case_44_]'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • %case_44_]

1,

replace '1,' by 'label'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • label

i32

replace 'i32' by '1,'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • 1,

%case_43_

replace '%case_43_' by 'i32'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • i32

label

replace 'label' by '%case_43_'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • %case_43_

0,

replace '0,' by 'label'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • label

i32

replace 'i32' by '0,'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • 0,

[

replace '[' by 'i32'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • i32

%end

replace '%end' by '['

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • [

label

replace 'label' by '%end'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • %end

41,

replace '41,' by 'label'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • label

i32

replace 'i32' by '41,'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • 41,

switch

replace 'switch' by 'i32'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • i32

3)

replace '3)' by 'switch'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • switch

@discriminate(i32

replace '@discriminate(i32' by '3)'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • 3)

i32

replace 'i32' by '@discriminate(i32'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • @discriminate(i32

wi

replace 'wi' by '='

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • =

entry:

replace 'entry:' by 'wi'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • wi

{

replace '{' by 'entry:'

Happy suggestion

define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of

define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4

Suggestions

  • entry:

insert '' by 'N.'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • N.

insert '' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

i.

replace 'i.' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

I

replace 'I' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

t

replace 't' by 'N.'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • N.

'

replace ''' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

I

replace 'I' by 'N.'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • N.

|

replace '|' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

1

replace '1' by 'N.'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • N.

*-------traversal

replace '*-------traversal' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

generation

replace 'generation' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

(e-graph

replace '(e-graph' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

:

replace ':' by 'Wasm'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • Wasm

e-graph

replace 'e-graph' by 'i.'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • i.

,

delete ',' by ''

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

a}

delete 'a}' by ''

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

>

replace '>' by 't'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • t

————

replace '————' by '''

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • '

insert '' by 'I'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • I

insert '' by 'wasm-mutate'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • wasm-mutate

insert '' by '1'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • 1

insert '' by '*-------traversal'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • *-------traversal

insert '' by 'generation'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • generation

insert '' by '(e-graph'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • (e-graph

insert '' by ':'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • :

insert '' by 'e-graph'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • e-graph

insert '' by ','

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • ,

insert '' by 'a'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • a

insert '' by '>'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • >

insert '' by '————'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • ————

'

replace ''' by 'I'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • I

'

replace ''' by '|'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • |

IR

replace 'IR' by '''

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • '

transformed*

replace 'transformed*' by '''

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • '

insert '' by 'transformed*'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • transformed*

binary

replace 'binary' by 'IR'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • IR

~

replace '~' by 'binary'

Happy suggestion

binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • binary

'

Don’t put a space on both sides of a quote symbol.

Happy suggestion

~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N

Suggestions

  • '
  • '

https://github.com/ASSERT-KTH/MEWE

replace 'https://github.com/ASSERT-KTH/MEWE' by 'https://github.com/ASSERT-KTH/MEWE.'

Happy suggestion

Contribution paper and artifact MEWE provides dynamic execution path randomization by packaging variants generated out of CROW. MEWE is fully presented in Cabrera-Arteaga et al. “Multi-Variant Execution at the Edge” Proceedings of Moving Target Defense, 2022, ACM https://dl.acm.o rg/doi/abs/10.1145/3560828. 3564007 MEWE is also available as an open-source tool at https://github.com/ASSERT-KTH/MEWE.

Contribution paper and artifact MEWE provides dynamic execution path randomization by packaging variants generated out of CROW. MEWE is fully presented in Cabrera-Arteaga et al. “Multi-Variant Execution at the Edge” Proceedings of Moving Target Defense, 2022, ACM https://dl.acm.o rg/doi/abs/10.1145/3560828. 3564007 MEWE is also available as an open-source tool at https://github.com/ASSERT-KTH/MEWE

Suggestions

  • https://github.com/ASSERT-KTH/MEWE.

meta-rule,

replace 'meta-rule,' by 'meta-rule'

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Rewriting rules inside the Peephole meta-rule operate over the data flow graph of instructions within a function body, representing the lowest level of rewriting. In WASM-MUTATE, we have implemented 125 rewriting rules specifically for this category, each one avoiding targeting instructions that might induce undefined behavior, e.g., function calls.

Rewriting rules inside the Peephole meta-rule, operate over the data flow graph of instructions within a function body, representing the lowest level of rewriting. In WASM-MUTATE, we have implemented 125 rewriting rules specifically for this category, each one avoiding targeting instructions that might induce undefined behavior, e.g., function calls.

Suggestions

  • meta-rule

enhancing

replace 'enhancing' by 'Enhancing'

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WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a triple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. Enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.

WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a tuple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.

Suggestions

  • Enhancing

tuple

replace 'tuple' by 'triple'

Happy suggestion

WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a triple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. Enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.

WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a tuple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.

Suggestions

  • triple

Rules

replace 'Rules' by 'Rules.'

Happy suggestion

WebAssembly Rewriting Rules.

WebAssembly Rewriting Rules

Suggestions

  • Rules.

I multiple

A verb or adverb may be missing or misspelled.

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Definition 2 (Stacked transformation) Given an original input WebAssembly binary I and a diversifier D, stacked transformations are defined as the application of D over the binary I multiple times, t.e., D(D(D(...(1)))). Notice that, the number of stacked transformations is the number of times the diversifier D is applied.

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transformation

replace 'transformation' by 'transformation.'

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WASM-MUTATE applies one transformation at a time. Notice that the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation.

WASM-MUTATE applies one transformation at a time. Notice that, the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation

Suggestions

  • transformation.

that,

replace 'that,' by 'that'

Happy suggestion

WASM-MUTATE applies one transformation at a time. Notice that the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation.

WASM-MUTATE applies one transformation at a time. Notice that, the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation

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  • that

an

replace 'an' by 'An'

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Figure 3.5 illustrates the workflow of WASM-MUTATE, which initiates with a WebAssembly binary as its input. The first step involves parsing this binary to create suitable abstractions, e.g. An intermediate representation. Subsequently, WASM-MUTATE uses predefined rewriting rules to construct an e-graph for the initial program, encapsulating all potential equivalent codes derived from the rewriting rules. The assurance of functional equivalence is rooted in the inherent properties of the individual rewrite rules employed. Then, pieces of the original program are randomly substituted by the result of random e-graph traversals, resulting in a variant that maintains functional equivalence to the original binary.

Figure 3.5 illustrates the workflow of WASM-MUTATE, which initiates with a WebAssembly binary as its input. The first step involves parsing this binary to create suitable abstractions, e.g. an intermediate representation. Subsequently, WASM-MUTATE uses predefined rewriting rules to construct an e-graph for the initial program, encapsulating all potential equivalent codes derived from the rewriting rules. The assurance of functional equivalence is rooted in the inherent properties of the individual rewrite rules employed. Then, pieces of the original program are randomly substituted by the result of random e-graph traversals, resulting in a variant that maintains functional equivalence to the original binary.

Suggestions

  • An

-i32.rand

replace '-i32.rand' by '-i32.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand'

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Cond y = x -i32.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand

Cond y = x -i32.rand

Suggestions

  • -i32.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand

x

replace 'x' by 'x.'

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LHS i32.const x.

LHS i32.const x

Suggestions

  • x.

Concretely,

replace 'Concretely,' by 'Concretly,'

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Moreover, we augment the internal representation of a Wasm program to bolster WASM-MUTATE’s transformation capabilities through the Peephole meta-rule. Concretly, we augment the parsing stage in WASM-MUTATE by including custom operator instructions. These custom operator instructions are designed to use well-established code diversification techniques through rewriting rules. When converting back to the WebAssembly binary format from the intermediate representation, custom instructions are meticulously handled to retain the original functionality of the WebAssembly program.

Moreover, we augment the internal representation of a Wasm program to bolster WASM-MUTATE’s transformation capabilities through the Peephole meta-rule. Concretely, we augment the parsing stage in WASM-MUTATE by including custom operator instructions. These custom operator instructions are designed to use well-established code diversification techniques through rewriting rules. When converting back to the WebAssembly binary format from the intermediate representation, custom instructions are meticulously handled to retain the original functionality of the WebAssembly program.

Suggestions

  • Concretly,

omitting

delete 'omitting' by ''

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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In

delete 'In' by ''

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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operands.

replace 'operands.' by 'Omitting'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • Omitting

e-class’s

replace 'e-class’s' by 'operands.'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • operands.

corresponding

replace 'corresponding' by 'e-class’s'

Happy suggestion

Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • e-class’s

the

replace 'the' by 'corresponding'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • corresponding

within

replace 'within' by 'the'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • the

options

replace 'options' by 'within'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • within

available

replace 'available' by 'options'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • options

the

replace 'the' by 'available'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • available

from

replace 'from' by 'the'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • the

size

replace 'size' by 'from'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • from

minimal

replace 'minimal' by 'size'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • size

the

replace 'the' by 'minimal'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • minimal

with

replace 'with' by 'the'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • the

AST

replace 'AST' by 'with'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • with

the

replace 'the' by 'AST'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • AST

selecting

replace 'selecting' by 'the'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • the

subsequently

replace 'subsequently' by 'selecting'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • selecting

e-node,

replace 'e-node,' by 'subsequently'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • subsequently

specific

replace 'specific' by 'e-node,'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • e-node,

a

replace 'a' by 'specific'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • specific

at

replace 'at' by 'a'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • a

extraction

replace 'extraction' by 'at'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • at

the

replace 'the' by 'extraction'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • extraction

commence

replace 'commence' by 'the'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • the

might

replace 'might' by 'commence'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • commence

one

replace 'one' by 'might'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • might

e-graph,

replace 'e-graph,' by 'one'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • one

an

replace 'an' by 'e-graph,'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • e-graph,

from

replace 'from' by 'an'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • an

code

replace 'code' by 'from'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • from

“optimal”

replace '“optimal”' by 'code'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

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  • code

the

replace 'the' by '“optimal”'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • “optimal”

extract

replace 'extract' by 'the'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • the

To

replace 'To' by 'extract'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • extract

[146].

replace '[146].' by 'To'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • To

sequence

replace 'sequence' by '[146].'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • [146].

application

replace 'application' by 'sequence'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • sequence

their

replace 'their' by 'application'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • application

of

replace 'of' by 'their'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • their

regardless

replace 'regardless' by 'of'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • of

rules,

replace 'rules,' by 'regardless'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • regardless

optimization

replace 'optimization' by 'rules,'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • rules,

multiple

replace 'multiple' by 'optimization'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • optimization

from

replace 'from' by 'multiple'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • multiple

optimal

replace 'optimal' by 'code'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • code

the

replace 'the' by 'optimal'

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Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • optimal

highlight

replace 'highlight' by 'Highlight'

Happy suggestion

Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.

Suggestions

  • Highlight

traversal

replace 'traversal' by 'traversal.'

Happy suggestion

E-Graph traversal.

E-Graph traversal

Suggestions

  • traversal.

insert '' by 'of'

Happy suggestion

Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8, a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instance of i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

Suggestions

  • of

instanceof

replace 'instanceof' by 'instance'

Happy suggestion

Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8, a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instance of i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

Suggestions

  • instance

3.8

replace '3.8' by '3.8,'

Happy suggestion

Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8, a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instance of i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

Suggestions

  • 3.8,

,)

Put a space after the comma.

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Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.

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  • , )

expr

delete 'expr' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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11:

delete '11:' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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{subexpr}

delete '{subexpr}' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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U

delete 'U' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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expr.operands

delete 'expr.operands' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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<

delete '<' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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10:

delete '10:' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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1)

delete '1)' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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delete '—' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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depth

delete 'depth' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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child,

delete 'child,' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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TRAVERSE(egraph,

delete 'TRAVERSE(egraph,' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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~

delete '~' by ''

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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subexpr

replace 'subexpr' by 'TRAVERSE(e'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • TRAVERSE(e

9:

replace '9:' by 'subexpr'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • subexpr

do

replace 'do' by '9:'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • 9:

node.children

replace 'node.children' by 'do'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • do

replace '€' by 'node.children'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • node.children

child

replace 'child' by '€'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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each

replace 'each' by 'child'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • child

for

replace 'for' by 'each'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • each

8

replace '8' by 'for'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • for

|])

replace '|])' by '8'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • 8

=

replace '=' by '|]'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • |]

operands

replace 'operands' by '='

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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  • =

(node,

replace '(node,' by 'operands'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

Suggestions

  • operands

<

replace '<' by '(node,'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

Suggestions

  • (node,

<—

replace '<—' by '—'

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Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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]

Unpaired symbol: ‘[’ seems to be missing

Happy suggestion

Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr

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node

Possible agreement error. The noun ‘node’ seems to be countable.

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The algorithm initiates at the e-class with the instruction i64.const 1, as seen in Listing 3.8. At (2), it randomly selects an equivalent node within the e-class, in this instance taking the i64.add node, resulting: expr = i64.add 1 r. As the traversal advances, it follows on the left operand of the previously chosen node, settling on the i64.const 0 node within the same e-class (3). Then, the right operand of the i64.add node is selected, selecting the container(4) operator yielding: expr = i64.or (i64.const 0 container (r nop)). The algorithm chooses the right operand of the container (5), which correlates to the initial instruction e-node highlighted in (6), culminating in the final expression: expr = i64.or (i64.const 0 container(i64.const 1 nop)) i64.const 1. As we proceed to the encoding phases, the container operator is ignored as a real Wasm instruction, finally resulting in the program in Listing 3.9.

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  • nodes

i64)))

replace 'i64)))' by 'i64))'

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(module (type (;0;) (func (param i32 £32) (result i64)) (func (;0;) (type 0) (param i32 £32) (result i64) i64.const 1)) Listing 3.8: Wasm function.

(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) i64.const 1)) Listing 3.8: Wasm function.

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  • i64))

(

Unpaired symbol: ‘)’ seems to be missing

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(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) i64.const 1)) Listing 3.8: Wasm function.

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3.6.

replace '3.6.' by '3.6'

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(module (type (;0;) (func (param i32 £32) (result i64)) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6 The textual format is folded for better understanding.

(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6. The textual format is folded for better understanding.

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  • 3.6

i64)))

replace 'i64)))' by 'i64))'

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(module (type (;0;) (func (param i32 £32) (result i64)) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6 The textual format is folded for better understanding.

(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6. The textual format is folded for better understanding.

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Algorithm

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Notice that, within the e-graph showcased in Figure 3.6, the container node maintains equivalence across all e-classes. Consequently, increasing the depth parameter in algorithm 1 would potentially escalate the number of viable variants infinitely.

Notice that, within the e-graph showcased in Figure 3.6, the container node maintains equivalence across all e-classes. Consequently, increasing the depth parameter in Algorithm 1 would potentially escalate the number of viable variants infinitely.

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Then there is the question of whether or not there is a need for it.

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selection

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Random e-node selection.

Random e-node selection

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selection

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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e-node

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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Random

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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relation

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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Operator-Operand

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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Left

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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  • Right

container

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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Operand

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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i

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.

oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection

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“https://bytecodealliance.org/

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Contribution paper and artifact WASM-MUTATE uses rewriting rules and random traversals over e-graphs to provide a binary-based solution for WebAssembly diversification. WASM-MUTATE is fully presented in Cabrera-Arteaga et al. “WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly” Computers & Security, 2024. https://www.sciencedirect.com/science/article/pii/S0167404824000 324. WASM-MUTATE is available at https://github.com/bytecodealliance/wasm-t ools/tree/main/crates/wasm-mutate as a contribution to the Bytecode Alliance organization “. The Bytecode Alliance is dedicated to creating secure new software foundations, building on standards such as WebAssembly and WASI. “https://bytecodealliance.org/”

Contribution paper and artifact WASM-MUTATE uses rewriting rules and random traversals over e-graphs to provide a binary-based solution for WebAssembly diversification. WASM-MUTATE is fully presented in Cabrera-Arteaga et al. “WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly” Computers & Security, 2024. https://www.sciencedirect.com/science/article/pii/S0167404824000 324. WASM-MUTATE is available at https://github.com/bytecodealliance/wasm-t ools/tree/main/crates/wasm-mutate as a contribution to the Bytecode Alliance organization “. The Bytecode Alliance is dedicated to creating secure new software foundations, building on standards such as WebAssembly and WASI. “https://bytecodealliance.org/

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Variants

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On the other hand, CROW and MEWE, by using enumerative synthesis, ensure that the generated variants are more preserved than the variants created by WASM-MUTATE. In other words, the transformations generated out of CROW and MEWE are virtually irreversible by JIT compilers, such as V8 and wasmtime. This phenomenon is highlighted in the Variants strength column of Table 3.1, where we show that CROW and MEWE generate variants with 96% of preservation against 75% of WASM-MUTATE. High preservation is especially important where the preservation of the diversification is crucial, e.g., to hinder reverse engineering.

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  • Variant's

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Tool Input Core Variants | Target Variants | Security applications in lh Strength CROW Source code or | Enumerative > 1k Code 96% Hinders static analysis and LLVM Ir synthesis section reverse engineering. With functional equivalence proved through SMT solvers MEWE Source code or | CROW, > 1k Code and | 96% Hinders static and LL VM Ir Multivariant Function dynamic analysis, reverse execution sections engineering and web timing-based attacks. WASM-Wasm binary | hand-made > 10k All Web-| 76% Hinders signature-MUTATE rewriting rules, Assembly based identification, and e-sections cache timing side-channel graph random traversals attacks.

Tool Input Core Variants | Target Variants | Security applications in lh Strength CROW Source code or | Enumerative > 1k Code 96% Hinders static analysis and LLVM Ir synthesis section reverse engineering. with functional equivalence proved through SMT solvers MEWE Source code or | CROW, > 1k Code and | 96% Hinders static and LL VM Ir Multivariant Function dynamic analysis, reverse execution sections engineering and web timing-based attacks. WASM-Wasm binary | hand-made > 10k All Web-| 76% Hinders signature-MUTATE rewriting rules, Assembly based identification, and e-sections cache timing side-channel graph random traversals attacks.

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applications

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Security applications.

Security applications

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and,

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WRN cr neha CROW, MEWE and WASM-MUTATE generate WebAssembly variants that can be used to enhance security. Overall, they generate variants that are suitable for hardening static and dynamic analysis, side-channel attacks, and thwarting signature-based identification.

WRN cr neha CROW, MEWE and WASM-MUTATE generate WebAssembly variants that can be used to enhance security. Overall, they generate variants that are suitable for hardening static and dynamic analysis, side-channel attacks, and, thwarting signature-based identification.

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increase

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Last but not least, our technical contributions enhance security against web timing-based attacks [147, 148] by creating variants that exhibit a wide range of execution times, including faster variants compared to the original program. This strategy is especially prominent in MEWE’s approach, which develops multivariants functioning on randomizing execution paths, thereby thwarting attempts at timing-based inference attacks [148]. Adding another layer of benefit from MEWE, the integration of diverse variants into multivariants can potentially disrupt dynamic reverse engineering tools such as symbolic executors [96]. Concretely, different control flows through a random discriminator, exponentially increasing the number of possible execution paths, making multivariant binaries virtually unexplorable.

Last but not least, our technical contributions enhance security against web timing-based attacks [147, 148] by creating variants that exhibit a wide range of execution times, including faster variants compared to the original program. This strategy is especially prominent in MEWE’s approach, which develops multivariants functioning on randomizing execution paths, thereby thwarting attempts at timing-based inference attacks [148]. Adding another layer of benefit from MEWE, the integration of diverse variants into multivariants can potentially disrupt dynamic reverse engineering tools such as symbolic executors [96]. Concretely, different control flows through a random discriminator, exponentially increase the number of possible execution paths, making multivariant binaries virtually unexplorable.

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Knuth

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— Donald Knuth.

— Donald Knuth

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FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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DIVERSIFICATION

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI

ASSESSING SOFTWARE DIVERSIFICATION FOR

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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for

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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Diversification

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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Software

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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application

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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the

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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illustrate

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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we

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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  • illustrate

chapter,

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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this

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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N

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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I

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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offensive

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Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.

offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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offensive

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offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both

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Then there is the question of whether or not there is a need for it.

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Then there is the question of whether or not there is a need for it.

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Then there is the question of whether or not there is a need for it.

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Then there is the question of whether or not there is a need for it.

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server

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User browser Remote mining pool or server.

User browser © Remote mining pool or server

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User browser Remote mining pool or server.

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User browser Remote mining pool or server.

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User browser Remote mining pool or server.

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User browser Remote mining pool or server.

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The primary malicious use of WebAssembly in browsers is cryptojacking [66]. This is due to the essence of cryptojacking, the faster the mining, the better. Let us illustrate how a malicious WebAssembly binary is involved in browser cryptojacking. Figure 4.1 illustrates a browser attack scenario: a practical WebAssembly cryptojacking attack consists of three components: a WebAssembly binary, a JavaScript wrapper, and a backend cryptominer pool. The WebAssembly binary is responsible for executing the hash calculations, which consume significant computational resources. The JavaScript wrapper facilitates the communication between the WebAssembly binary and the cryptominer pool.

The primary malicious use of WebAssembly in browsers is cryptojacking [66]. This is due to the essence of cryptojacking, the faster the mining, the better. Let us illustrate how a malicious WebAssembly binary is involved into browser cryptojacking. Figure 4.1 illustrates a browser attack scenario: a practical WebAssembly cryptojacking attack consists of three components: a WebAssembly binary, a JavaScript wrapper, and a backend cryptominer pool. The WebAssembly binary is responsible for executing the hash calculations, which consume significant computational resources. The JavaScript wrapper facilitates the communication between the WebAssembly binary and the cryptominer pool.

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Offensive Diversification: Malware evasion.

Offensive Diversification: Malware evasion

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Cryptojacking defense evasion.

Cryptojacking defense evasion

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Both antivirus software and browsers have implemented measures to detect cryptojacking. For instance, Firefox employs deny lists to detect cryptomining activities [149]. The academic community has also contributed to the body of work on detecting or preventing WebAssembly-based cryptojacking, as outlined in Section 2.1.5. However, malicious actors can employ evasion techniques to circumvent these detection mechanisms. Bhansali et al. They are among the first who have investigated how WebAssembly cryptojacking could potentially evade detection [67], highlighting the critical importance of this use case. The case illustrated in the subsequent sections uses Offensive Software Diversification for evading malware detection in WebAssembly.

Both antivirus software and browsers have implemented measures to detect cryptojacking. For instance, Firefox employs deny lists to detect cryptomining activities [149]. The academic community has also contributed to the body of work on detecting or preventing WebAssembly-based cryptojacking, as outlined in Section 2.1.5. However, malicious actors can employ evasion techniques to circumvent these detection mechanisms. Bhansali et al. are among the first who have investigated how WebAssembly cryptojacking could potentially evade detection [67], highlighting the critical importance of this use case. The case illustrated in the subsequent sections uses Offensive Software Diversification for evading malware detection in WebAssembly.

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@ User browser ‘QO Malware @ Remote detector QY) mining pool or server Malware) evasion technique BI)

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Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method

Methodology

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Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method

Methodology

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1. The user loads a webpage infected with cryptojacking malware, which leverages network resources for execution—corresponding to qa) and 2) in Figure 4.2.

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Based on the two types of malware oracles and diversification configurations, we examine three scenarios: 1) VirusTotal with a feedback-guided diversification, 2) VirusTotal with a stochastic diversification, and 3) MINOS with a stochastic diversification. Notice that the fourth scenario with MINOS and feedback-guided diversification is not feasible, as MINOS is a binary oracle and cannot provide the numerical values required for feedback-guided diversification.

Based on the two types of malware oracles and diversification configurations, we examine three scenarios: 1) VirusTotal with a feedback-guided diversification, 2) VirusTotal with a stochastic diversification, and 3) MINOS with a stochastic diversification. Notice that, the fourth scenario with MINOS and feedback-guided diversification is not feasible, as MINOS is a binary oracle and cannot provide the numerical values required for feedback-guided diversification.

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Definition 4 (Feedback-guided diversification) In feedback-guided diversification, the transformation process of a WebAssembly program is guided by a numeric oracle, which influences the probability of each transformation. For instance, WASM-MUTATE can be configured to apply transformations that minimize the oracle’s confidence score. Note that feedback-guided diversification needs a numeric oracle.

Definition 4 (Feedback-guided Diversification) In feedback-guided diversification, the transformation process of a WebAssembly program is guided by a numeric oracle, which influences the probability of each transformation. For instance, WASM-MUTATE can be configured to apply transformations that minimize the oracle’s confidence score. Note that feedback-guided diversification needs a numeric oracle.

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identify

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We use the wasmbench dataset [45] to establish a ground truth. After running the wasmbench dataset through VirusTotal and MINOS, we identified 33 binaries that are: 1) flagged as malicious by at least one VirusTotal vendor and, 2) are also detected by MINOS. Then, to simulate the evasion scenario in Figure 4.2, we use WASM-MUTATE to generate WebAssembly binary variants to evade malware detection ((4) in Figure 4.2). We use WASM-MUTATE in two configurations: feedback-guided and stochastic diversification.

We use the wasmbench dataset [45] to establish a ground truth. After running the wasmbench dataset through VirusTotal and MINOS, we identify 33 binaries that are: 1) flagged as malicious by at least one VirusTotal vendor and, 2) are also detected by MINOS. Then, to simulate the evasion scenario in Figure 4.2, we use WASM-MUTATE to generate WebAssembly binary variants to evade malware detection ((4) in Figure 4.2). We use WASM-MUTATE in two configurations: feedback-guided and stochastic diversification.

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Results

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Results:

Results

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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MINOS

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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lower,

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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diversification

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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stochastic

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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text

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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We

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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(Mean

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • #trans.)

of

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • (Mean

number

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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average

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • transformations

the

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • of

and

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • number

#evaded),

replace '#evaded),' by 'average'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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(Max.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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detectors

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • and

evaded

replace 'evaded' by '#evaded),'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • #evaded),

successfully

replace 'successfully' by '(Max.'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • (Max.

of

replace 'of' by 'detectors'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • detectors

number

replace 'number' by 'evaded'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • evaded

maximum

replace 'maximum' by 'successfully'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • successfully

(#D),

replace '(#D),' by 'number'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • number

malware

replace 'malware' by 'maximum'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • maximum

as

replace 'as' by 'the'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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binary

replace 'binary' by '(#D),'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • (#D),

original

replace 'original' by 'malware'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • malware

the

replace 'the' by 'as'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • as

flag

replace 'flag' by 'binary'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • binary

that

replace 'that' by 'original'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • original

vendors

replace 'vendors' by 'the'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • the

of

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • that

number

replace 'number' by 'vendors'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • vendors

the

replace 'the' by 'VirusTotal'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • VirusTotal

indicate

replace 'indicate' by 'of'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • of

columns

replace 'columns' by 'number'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • number

subsections,

delete 'subsections,' by ''

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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these

replace 'these' by 'indicate'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • indicate

In

replace 'In' by 'columns'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • columns

diversification.

replace 'diversification.' by 'the'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • the

feedback-guided

replace 'feedback-guided' by 'subsections,'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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and

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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stochastic

replace 'stochastic' by 'In'

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • In

employ:

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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we

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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configurations

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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diversification

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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two

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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the

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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results

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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131

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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218

delete '218' by ''

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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Median

delete 'Median' by ''

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

15

delete '15' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

488

delete '488' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

1

delete '1' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

120

delete '120' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

e09c32c5

delete 'e09c32c5' by ''

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

33

delete '33' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

2

delete '2' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

118

delete '118' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

5bc53343

delete '5bc53343' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

11

delete '11' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

127

delete '127' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

f0b24409

delete 'f0b24409' by ''

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

137

delete '137' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

68ca7c0e

delete '68ca7c0e' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

145

delete '145' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

a27b45ef

delete 'a27b45ef' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

143

delete '143' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

6b8c7899

delete '6b8c7899' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

66

delete '66' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

132

delete '132' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

140

delete '140' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

dceaf65b

delete 'dceaf65b' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

130

delete '130' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

clbe4071

delete 'clbe4071' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

68

delete '68' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

123

delete '123' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

153

delete '153' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

089dd312

delete '089dd312' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

it

delete 'it' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

18

delete '18' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

104

delete '104' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

119c53eb

delete '119c53eb' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

124

delete '124' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

a74a7cb8

delete 'a74a7cb8' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

108

delete '108' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

107

delete '107' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

431

delete '431' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

89a3645c

delete '89a3645c' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

59955b4c

delete '59955b4c' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

65debcbe

delete '65debcbe' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

il

delete 'il' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

72

delete '72' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

3.

delete '3.' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

295

delete '295' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

3

delete '3' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

4cbdbbb1

delete '4cbdbbb1' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

960

delete '960' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

34

delete '34' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

302

replace '302' by '6b'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 6b

3

replace '3' by '66'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 66

3

replace '3' by '132'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 132

000415b2

replace '000415b2' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

980

replace '980' by '140'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 140

401

replace '401' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

4

replace '4' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

252

replace '252' by 'dceaf65b'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • dceaf65b

4

replace '4' by '38'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 38

4

replace '4' by '33'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 33

24aael3a

replace '24aael3a' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

il

replace 'il' by '130'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 130

475

replace '475' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

297

replace '297' by 'clbe4071'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • clbe4071

4

replace '4' by '68'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 68

4

replace '4' by '123'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 123

fb15929f

replace 'fb15929f' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

85

replace '85' by '153'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 153

221

replace '221' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

236

replace '236' by '089dd312'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 089dd312

4

replace '4' by 'it'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • it

4

replace '4' by '18'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 18

7c36f462

replace '7c36f462' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

29

replace '29' by '104'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 104

200

replace '200' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

200

replace '200' by '119c53eb'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 119c53eb

942be4f7

replace '942be4f7' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

HI

replace 'HI' by '124'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 124

380

replace '380' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

282

replace '282' by 'a74a7cb8'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • a74a7cb8

4

replace '4' by '108'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 108

4

replace '4' by '107'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 107

006b2fb6

replace '006b2fb6' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

lh

replace 'lh' by '431'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 431

1381

replace '1381' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

253

replace '253' by '89a3645c'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 89a3645c

15b86a25

replace '15b86a25' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

47

replace '47' by '130'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 130

436

replace '436' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

6

replace '6' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

144

replace '144' by '59955b4c'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 59955b4c

6

replace '6' by '38'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 38

6

replace '6' by '33'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 33

643116fF

replace '643116fF' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

33

replace '33' by '131'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 131

159

replace '159' by '2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 2

404

replace '404' by '65debcbe'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 65debcbe

6

replace '6' by 'il'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • il

6

replace '6' by '72'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 72

046dc081

replace '046dc081' by '643116fF'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 643116fF

1

replace '1' by '33'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 33

331

replace '331' by '159'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 159

484

replace '484' by '404'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 404

aafff587

replace 'aafff587' by '046dc081'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 046dc081

81

replace '81' by '1'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 1

781

replace '781' by '331'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 331

9

replace '9' by '6'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 6

461

replace '461' by '484'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 484

d2141ff2

replace 'd2141ff2' by 'aafff587'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • aafff587

1

replace '1' by '81'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 81

726

replace '726' by '781'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 781

18

replace '18' by '9'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 9

310

replace '310' by '461'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 461

fbddlefa

replace 'fbddlefa' by 'd2141ff2'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • d2141ff2

625

replace '625' by '726'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 726

635

replace '635' by '310'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 310

a32a6f4b

replace 'a32a6f4b' by 'fbddlefa'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • fbddlefa

24

replace '24' by '1'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 1

697

replace '697' by '625'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 625

19

replace '19' by '18'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 18

401

replace '401' by '635'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 635

0d996462

replace '0d996462' by 'a32a6f4b'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • a32a6f4b

115

replace '115' by '24'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 24

446

replace '446' by '697'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 697

20

replace '20' by '19'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 19

355

replace '355' by '401'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 401

c11d82d

replace 'c11d82d' by '0d996462'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 0d996462

92

replace '92' by '115'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 115

N/A

replace 'N/A' by '446'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 446

13.

replace '13.' by '20'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 20

N/A

replace 'N/A' by '355'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 355

21

replace '21' by '20'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 20

26

replace '26' by '20'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 20

8ebf4e44

replace '8ebf4e44' by 'c11d82d'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • c11d82d

419

replace '419' by '92'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 92

17

replace '17' by '13.'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 13.

24

replace '24' by '21'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 21

30

replace '30' by '26'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 26

9d30e7f0

replace '9d30e7f0' by '8ebf4e44'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 8ebf4e44

100

replace '100' by '419'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 419

19

replace '19' by '17'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 17

26

delete '26' by ''

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

31

replace '31' by '24'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 24

47d29959

replace '47d29959' by '30'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 30

trans.

replace 'trans.' by '9d30e7f0'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 9d30e7f0

Mean

replace 'Mean' by '100'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 100

|

replace '|' by 'N/A'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • N/A

trans.

replace 'trans.' by '19'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 19

Mean

replace 'Mean' by 'N/A'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • N/A

evaded

replace 'evaded' by '26'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 26

Max.

replace 'Max.' by '31'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 31

|

replace '|' by '47d29959'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • 47d29959

insert '' by '|'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • |

insert '' by 'trans.'

Happy suggestion

VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

Suggestions

  • trans.

insert '' by 'Mean'

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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  • Mean

div.

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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  • evaded

Feedback-guided

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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  • Max.

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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  • Feedback-guided

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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  • div.

MINOS

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VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b

VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38

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  • Minos

MINOS.

replace 'MINOS.' by 'Minos.'

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Table 4.1: The table has two main categories for each malware oracle, corresponding to the two oracles we use: VirusTotal and Minos. For VirusTotal, divide the results based on the two diversification configurations: stochastic and feedback-guided diversification. We provide columns that indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which diversification setups are best, where the lower, the better. The MINOS section includes a column that specifies the number of transformations needed for complete evasion. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

Table 4.1: The table has two main categories for each malware oracle, corresponding to the two oracles we use: VirusTotal and MINOS. For VirusTotal, divide the results based on the two diversification configurations: stochastic and feedback-guided diversification. We provide columns that indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which diversification setups are best, where the lower, the better. The MINOS section includes a column that specifies the number of transformations needed for complete evasion. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.

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  • Minos.

the

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Feedback-guided diversification to evade VirusTotal: stochastic diversification does not guide the diversification based on the number of evaded detectors, it is purely random and has some drawbacks. For example, some transformations might suppress other transformations previously applied. We have observed that, by carefully selecting the order and type of transformations applied, it is possible to evade detection systems in fewer iterations. This can be appreciated in the results of the feedback-guided diversification part of Table 4.1. The feedback-guided diversification setup successfully generates variants that totally evade detection for 30 out of 33 binaries, it is thus as good as the stochastic setup. Remarkably, for 21 binaries out of 30, feedback-guided needs only 40% of the calls the stochastic diversification setup needs, demonstrating larger efficiency. Moreover, the lower number of transformations needed to evade detection, compared to the stochastic diversification setup, highlights the efficacy of the feedback-guided diversification setup in studying effective transformations. Consequently, malware detection system developers can leverage feedback-guided diversification to enhance their systems, focusing on identifying specific transformations.

Feedback-guided diversification to evade VirusTotal: stochastic diversification does not guide the diversification based on the number of evaded detectors, it is purely random and has some drawbacks. For example, some transformations might suppress other transformations previously applied. We have observed that, by carefully selecting the order and type of transformations applied, it is possible to evade detection systems in fewer iterations. This can be appreciated in the results of the feedback-guided diversification part of Table 4.1. The feedback-guided diversification setup successfully generates variants that totally evade the detection for 30 out of 33 binaries, it is thus as good as the stochastic setup. Remarkably, for 21 binaries out of 30, feedback-guided needs only 40% of the calls the stochastic diversification setup needs, demonstrating larger efficiency. Moreover, the lower number of transformations needed to evade detection, compared to the stochastic diversification setup, highlights the efficacy of the feedback-guided diversification setup in studying effective transformations. Consequently, malware detection system developers can leverage feedback-guided diversification to enhance their systems, focusing on identifying specific transformations.

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2.1.5

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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Section

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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In

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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VirusTotal.

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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within

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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vendors

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • 2.1.5,

general-purpose

replace 'general-purpose' by 'Section'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • Section

the

replace 'the' by 'In'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • In

from

replace 'from' by 'VirusTotal.'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • VirusTotal.

differ

replace 'differ' by 'within'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • within

which

replace 'which' by 'vendors'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • vendors

WebAssembly,

replace 'WebAssembly,' by 'general-purpose'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • general-purpose

for

replace 'for' by 'the'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • the

solutions

replace 'solutions' by 'from'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • from

specialized

replace 'specialized' by 'differ'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • differ

of

replace 'of' by 'which'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • which

existence

replace 'existence' by 'WebAssembly,'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • WebAssembly,

the

replace 'the' by 'for'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • for

given

replace 'given' by 'solutions'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • solutions

particularly

replace 'particularly' by 'specialized'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • specialized

issues,

replace 'issues,' by 'of'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • of

pose

replace 'pose' by 'existence'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • existence

could

replace 'could' by 'the'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Suggestions

  • the

detection

replace 'detection' by 'given'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • given

for

replace 'for' by 'particularly'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • particularly

on

replace 'on' by 'pose'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • pose

exclusively

replace 'exclusively' by 'could'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • could

Relying

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • detection

MINOS:

replace 'MINOS:' by 'for'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • for

evade

replace 'evade' by 'VirusTotal'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • VirusTotal

to

replace 'to' by 'on'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • on

diversification

replace 'diversification' by 'exclusively'

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Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • exclusively

Stochastic

replace 'Stochastic' by 'Relying'

Happy suggestion

Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.

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  • Relying

correctness:

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To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.

WebAssembly variants correctness: To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.

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variants

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To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.

WebAssembly variants correctness: To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.

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protection

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Defensive Diversification: speculative side-channel protection.

Defensive Diversification: speculative side-channel protection

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the

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Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.

Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.

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sections)

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Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.

Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.

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custom

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Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.

Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.

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(WebAssembly

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Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.

Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.

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4.3).

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The fundamental advantage of using WebAssembly in FaaS platforms lies in its ability to encapsulate thousands of WebAssembly binaries within a singular host process. A developer could compile its source code into a WebAssembly program suitable for the cloud platform and then submit it (in Figure 4.3). This host process is then disseminated across a network of servers and data centers (2) in Figure 4.3. These platforms convert WebAssembly programs into native code, which is subsequently executed in a sandboxed environment. Host processes can then instantiate new WebAssembly sandboxes for each client function, executing them in response to specific user requests with nanosecond-level latency (@) in Figure 4.3. This architecture inherently isolates WebAssembly binary executions from each other as well as from the host process, enhancing security.

The fundamental advantage of using WebAssembly in FaaS platforms lies in its ability to encapsulate thousands of WebAssembly binaries within a singular host process. A developer could compile its source code into a WebAssembly program suitable for the cloud platform and then submit it (in Figure 4.3). This host process is then disseminated across a network of servers and data centers (2) in Figure 4.3). These platforms convert WebAssembly programs into native code, which is subsequently executed in a sandboxed environment. Host processes can then instantiate new WebAssembly sandboxes for each client function, executing them in response to specific user requests with nanosecond-level latency (@) in Figure 4.3). This architecture inherently isolates WebAssembly binary executions from each other as well as from the host process, enhancing security.

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\

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Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 1 1 !

Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 ' 1 1 ! \

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'

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Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 1 1 !

Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 ' 1 1 ! \

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,

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Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 ' 1 1 ! \

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4.3).

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However, while WebAssembly is engineered with a strong focus on security and isolation, it is not entirely immune to vulnerabilities such as Spectre attacks [151, 142] () in Figure 4.3. In the sections that follow, we explore how software diversification techniques can be employed to harden WebAssembly binaries against such attacks.

However, while WebAssembly is engineered with a strong focus on security and isolation, it is not entirely immune to vulnerabilities such as Spectre attacks [151, 142] () in Figure 4.3). In the sections that follow, we explore how software diversification techniques can be employed to harden WebAssembly binaries against such attacks.

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attacks

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Threat model: speculative side-channel attacks.

Threat model: speculative side-channel attacks

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process

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Source Code Software Diversification Developer Computer request HTTP Memory isolation M bound. is violated MEY OUNCE, X86 Host _ Cloud node process.

Source Code Software Diversification Developer Computer request HTTP Memory isolation M bound. is violated MEY OUNCE, X86 Host _ Cloud node process

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To empirically demonstrate that Software Diversification can indeed mitigate Spectre vulnerabilities, we reuse the WebAssembly attack scenarios proposed by Narayan et al. In their work on Swivel [63]. Swivel is a compiler-based strategy designed to counteract Spectre attacks on WebAssembly binaries by linearizing their control flow during machine code compilation. Our approach differs from theirs in that it is binary-based, compiler-agnostic, and platform-agnostic; we do not propose altering the deployment or toolchain of FaaS platforms.

To empirically demonstrate that Software Diversification can indeed mitigate Spectre vulnerabilities, we reuse the WebAssembly attack scenarios proposed by Narayan et al. in their work on Swivel [63]. Swivel is a compiler-based strategy designed to counteract Spectre attacks on WebAssembly binaries by linearizing their control flow during machine code compilation. Our approach differs from theirs in that it is binary-based, compiler-agnostic, and platform-agnostic; we do not propose altering the deployment or toolchain of FaaS platforms.

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insert '' by 'Method'

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Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method

Methodology

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  • Method

insert '' by 'Methodology'

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Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method

Methodology

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  • Methodology

Results

replace 'Results' by 'Results:'

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Results:

Results

Suggestions

  • Results:

attacks

replace 'attacks' by 'attacks,'

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The previous metric not only captures the success or failure of the attacks, but also quantifies the extent to which data exfiltration is hindered. For example, a variant that still leaks data but does so at an impractically slow rate would be considered hardened against the attack.

The previous metric not only captures the success or failure of the attacks but also quantifies the extent to which data exfiltration is hindered. For example, a variant that still leaks data but does so at an impractically slow rate would be considered hardened against the attack.

Suggestions

  • attacks,

delete '—' by ''

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Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.

Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —

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———

replace '———' by '———————.'

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Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.

Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —

Suggestions

  • ———————.

01,...,bc}

replace '01,...,bc}' by '01,...,bc'

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Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.

Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —

Suggestions

  • 01,...,bc

{bo,

replace '{bo,' by 'bo,'

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Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.

Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —

Suggestions

  • bo,

,bc

Put a space after the comma.

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Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —

Suggestions

  • , bc

(pht)

replace '(pht)' by '(pht).'

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Program Attack btb_breakout | Spectre branch target buffer (btb) btb_leakage | Spectre branch target buffer (btb) ret2spec Spectre Return Stack Buffer (rsb sb) pht Spectre Pattern History Table (pht).

Program Attack btb_breakout | Spectre branch target buffer (btb) btb_leakage | Spectre branch target buffer (btb) ret2spec Spectre Return Stack Buffer (rsb sb) pht Spectre Pattern History Table (pht)

Suggestions

  • (pht).

transformations

delete 'transformations' by ''

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pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

Stacked

delete 'Stacked' by ''

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pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

Wpeapueg,

delete 'Wpeapueg,' by ''

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

(8/4)

delete '(8/4)' by ''

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

8

delete '8' by ''

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

2

delete '2' by ''

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

8

replace '8' by 'Wapeapueg,'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Wapeapueg,

ge

replace 'ge' by '(8/4)'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • (8/4)

replace '—' by '8'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 8

*

replace '*' by '2'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 2

I

replace 'I' by '8'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 8

———

replace '———' by 'ge'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • ge

EN

replace 'EN' by '—'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

ee

replace 'ee' by '*'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • *

SSL

replace 'SSL' by 'I'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • I

a,

replace 'a,' by '———'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • ———

*s,,

replace '*s,,' by 'SSL'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • SSL

ee

replace 'ee' by 'a,'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • a,

*,

replace '*,' by '*s,,'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • *s,,

a

replace 'a' by 'ee'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • ee

%

replace '%' by '*,'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • *,

Se,

replace 'Se,' by 'a'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • a

=

replace '=' by '%'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • %

*,

replace '*,' by 'Se,'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Se,

=e

replace '=e' by '='

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • =

1

replace '1' by '=e'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • =e

*:

replace '*:' by '%'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • %

[>

replace '[>' by '1'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 1

*

replace '*' by '*:'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • *:

I

replace 'I' by '[>'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • [>

a

replace 'a' by '*'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • *

0.0

replace '0.0' by 'I'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • I

8

replace '8' by 'a'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • a

1.0

replace '1.0' by '0.0'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 0.0

Wapeapueg

replace 'Wapeapueg' by '8'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 8

(8/4)

replace '(8/4)' by '1.0'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 1.0

Ss

replace 'Ss' by 'Wapeapueg'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Wapeapueg

Ss

replace 'Ss' by '(8/4)'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • (8/4)

os

replace 'os' by 'Ss'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Ss

2

replace '2' by 'Ss'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Ss

2

replace '2' by 'os'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • os

4

replace '4' by '2'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 2

‘HM

replace '‘HM' by '2'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 2

Ss

replace 'Ss' by '4'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • 4

=

replace '=' by '‘HM'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • ‘HM

&

replace '&' by 'Ss'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Ss

s

replace 's' by '='

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • =

*

replace '*' by '&'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • &

=

replace '=' by 's'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • s

SF

replace 'SF' by '*'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • *

SF

replace 'SF' by '='

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • =

Ss

replace 'Ss' by 'SF'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • SF

insert '' by 'Ss'

Happy suggestion

pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked

pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations

Suggestions

  • Ss

Strength:

delete 'Strength:' by ''

Happy suggestion

For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.

Population Strength: For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.

Suggestions

Population

delete 'Population' by ''

Happy suggestion

For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.

Population Strength: For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.

Suggestions

transformations

delete 'transformations' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

Stacked

delete 'Stacked' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

oS

delete 'oS' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

PF

delete 'PF' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

FFF

delete 'FFF' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

SMe

replace 'SMe' by 'transformations.'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • transformations.

S

replace 'S' by 'Stacked'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Stacked

LSS

replace 'LSS' by 'transformations'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • transformations

FFF

replace 'FFF' by 'Stacked'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Stacked

SF

replace 'SF' by 'oS'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • oS

S

replace 'S' by 'PF'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • PF

SF

replace 'SF' by 'FFF'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • FFF

©

replace '©' by 'SMe'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • SMe

FF

replace 'FF' by 'S'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • S

Ss

replace 'Ss' by 'LSS'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • LSS

se

replace 'se' by 'FFF'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • FFF

Ss

replace 'Ss' by 'SF'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • SF

s

replace 's' by 'S'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • S

SF

delete 'SF' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

-=

delete '-=' by ''

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

insert '' by 'Ss'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Ss

insert '' by 'FF'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • FF

El)

replace 'El)' by 'SF'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • SF

Weve.

replace 'Weve.' by 'Ss'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Ss

a

replace 'a' by '-='

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • -=

8

replace '8' by 's'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • s

a

replace 'a' by 'El)'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • El)

S

replace 'S' by 'Weve.'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Weve.

(b/s)

replace '(b/s)' by 'a'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • a

Bandwidth

replace 'Bandwidth' by '8'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • 8

So

replace 'So' by 'a'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • a

insert '' by '(b/s)'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • (b/s)

insert '' by 'Bandwidth'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Bandwidth

on

replace 'on' by 'So'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • So

insert '' by 'on'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • on

uw

replace 'uw' by 'S'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • S

>

replace '>' by 'uw'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • uw

Nn

replace 'Nn' by '>'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • >

~

replace '~' by 'Nn'

Happy suggestion

N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • Nn

S S

Possible typo: you repeated a word

Happy suggestion

N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations

Suggestions

  • S

insert '' by 'it.'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • it.

insert '' by 'for'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • for

insert '' by 'need'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • need

insert '' by 'a'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • a

insert '' by 'is'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • is

insert '' by 'there'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • there

insert '' by 'not'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • not

insert '' by 'or'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • or

insert '' by 'whether'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • whether

insert '' by 'of'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • of

insert '' by 'question'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • question

insert '' by 'the'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • the

insert '' by 'Then'

Happy suggestion

Then there is the question of whether or not there is a need for it.

Suggestions

  • Then

programs

replace 'programs' by 'programs,'

Happy suggestion

Figure 4.6: Impact of WASM-MUTATE over ret2spec and pht binaries. The Y-axis denotes eaxfiltration bandwidth, with the original binary’s bandwidth under attack highlighted by a blue marker and dashed line. Variants are clustered in groups of 100 stacked transformations, denoted by green violinplots. Overall, for both programs, approximately 70% of the variants have less data leakage bandwidth.

Figure 4.6: Impact of WASM-MUTATE over ret2spec and pht binaries. The Y-axis denotes eaxfiltration bandwidth, with the original binary’s bandwidth under attack highlighted by a blue marker and dashed line. Variants are clustered in groups of 100 stacked transformations, denoted by green violinplots. Overall, for both programs approximately 70% of the variants have less data leakage bandwidth.

Suggestions

  • programs,

btb_breakout

replace 'btb_breakout' by 'btb_breakout.'

Happy suggestion

Code from original btb_breakout.

Code from original btb_breakout

Suggestions

  • btb_breakout.

insert '' by 'cache'

Happy suggestion

Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Suggestions

  • cache

insert '' by 'measure'

Happy suggestion

Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Suggestions

  • measure

Cache

replace 'Cache' by 'to'

Happy suggestion

Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Suggestions

  • to

timers:

replace 'timers:' by 'timers'

Happy suggestion

Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.

Suggestions

  • timers

WASM-MUTATE:

delete 'WASM-MUTATE:' by ''

Happy suggestion

As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.

Effectiveness of WASM-MUTATE: As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.

Suggestions

of

delete 'of' by ''

Happy suggestion

As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.

Effectiveness of WASM-MUTATE: As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.

Suggestions

Effectiveness

delete 'Effectiveness' by ''

Happy suggestion

As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.

Effectiveness of WASM-MUTATE: As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.

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optimization

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Train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization.

train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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  • optimization.

train

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Train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization.

train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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  • Train

train

This sentence does not start with an uppercase letter.

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train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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  • Train

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(call $readTimer) (set_local $end_time) inserted instructions> access to mem inserted instructions> (i64.sub (get_local $end_time) (get_local $start_time)) (set_local $duration)

(call $readTimer) (set_local $end_time) access to mem (i64.sub (get_local $end_time) (get_local $start_time)) (set_local $duration)

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  • inserted

code

replace 'code' by 'code.'

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Variant code.

Variant code

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  • code.

transiently

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Transiently jump here (132.load (i32.const 339968)) S(83) is the secret (i32.store (i32.const 83)) just prevent optimization Listing 4.3: Two jump locations. The top one trains the branch predictor, the bottom one is the expected jump that exfiltrates the memory access.

transiently jump here (132.load (i32.const 339968)) S(83) is the secret (i32.store (i32.const 83)) just prevent optimization Listing 4.3: Two jump locations. The top one trains the branch predictor, the bottom one is the expected jump that exfiltrates the memory access.

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  • Transiently

transiently

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transiently jump here (132.load (i32.const 339968)) S(83) is the secret (i32.store (i32.const 83)) just prevent optimization Listing 4.3: Two jump locations. The top one trains the branch predictor, the bottom one is the expected jump that exfiltrates the memory access.

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4.4:

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Listing 4.4 WebAssembly variant with more instructions added indindinctly between jump places.

Listing 4.4: WebAssembly variant with more instructions added indindinctly between jump places.

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optimization

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Transiently jump here inserted instructions> (i32.load (i32.const 339968)) inserted instructions> (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization.

transiently jump here (i32.load (i32.const 339968)) (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization

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  • optimization.

replace '

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Transiently jump here inserted instructions> (i32.load (i32.const 339968)) inserted instructions> (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization.

transiently jump here (i32.load (i32.const 339968)) (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization

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  • inserted

transiently

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Transiently jump here inserted instructions> (i32.load (i32.const 339968)) inserted instructions> (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization.

transiently jump here (i32.load (i32.const 339968)) (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization

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  • Transiently

transiently

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transiently jump here (i32.load (i32.const 339968)) (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization

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optimization

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Train the code to jump here (index 1) inserted instructions> (i32.load (i32.const 2000)) inserted instructions> (i32.store (i32.const 83)) just prevent optimization.

train the code to jump here (index 1) (i32.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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  • optimization.

replace '

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Train the code to jump here (index 1) inserted instructions> (i32.load (i32.const 2000)) inserted instructions> (i32.store (i32.const 83)) just prevent optimization.

train the code to jump here (index 1) (i32.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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  • inserted

train

replace 'train' by 'Train'

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Train the code to jump here (index 1) inserted instructions> (i32.load (i32.const 2000)) inserted instructions> (i32.store (i32.const 83)) just prevent optimization.

train the code to jump here (index 1) (i32.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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  • Train

train

This sentence does not start with an uppercase letter.

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train the code to jump here (index 1) (i32.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization

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impact:

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The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.

Managed memory impact: The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.

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memory

delete 'memory' by ''

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The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.

Managed memory impact: The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.

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Managed

delete 'Managed' by ''

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The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.

Managed memory impact: The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.

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Diversification

replace 'Diversification' by 'diversification'

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In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, defensive software diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.

In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, Defensive Software Diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.

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  • diversification

Software

replace 'Software' by 'software'

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In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, defensive software diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.

In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, Defensive Software Diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.

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  • software

Defensive

replace 'Defensive' by 'defensive'

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In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, defensive software diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.

In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, Defensive Software Diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.

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  • defensive

Conclusions

replace 'Conclusions' by 'Conclusions.'

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Conclusions.

Conclusions

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  • Conclusions.

contributions

replace 'contributions' by 'contributions.'

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Summary of technical contributions.

Summary of technical contributions

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  • contributions.

This

replace 'This' by 'thing.'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • thing.

techniques.

replace 'techniques.' by 'good'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • good

hardening

replace 'hardening' by 'a'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • a

requires

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • is

of

replace 'of' by 'techniques.'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • techniques.

adoption

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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growing

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • requires

‘i

replace '‘i' by 'WebAssembly'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • WebAssembly

Diversification.

replace 'Diversification.' by 'of'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • of

Software

replace 'Software' by 'adoption'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • adoption

in

replace 'in' by '‘i'

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • ‘i

directions

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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  • Diversification.

research

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.

thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This

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— Donald Knuth.

— Donald Knuth

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WORK

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CONCLUSIONS AND FUTURE WORK.

CONCLUSIONS AND FUTURE WORK

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We have developed three open-source tools that are publicly accessible: Crow, MEWE, and WASM-MUTATE. CROW and MEWE explore a compiler-based approach, whereas WASM-MUTATE employs a method based on binary. These tools automate the process of diversification, thereby increasing their practicality for deployment. At present, WASM-MUTATE is integrated into the wasmtime project! to improve testing. Our tools are complementary, providing combined utility. For instance, when the source code for a WebAssembly binary is unavailable, WASM-MUTATE offers an efficient solution for the generation of code variants. On the other hand, CROW and MEWE are particularly suited for scenarios that require a high level of variant preservation. Finally, one can use CROW and MEWE to generate a set of variants, which can then serve as rewriting rules for WASM-MUTATE. Moreover, when practitioners need to quickly generate variants, they could employ WASM-MUTATE, despite a potential decrease in the preservation of variants.

We have developed three open-source tools that are publicly accessible: CROW, MEWE, and WASM-MUTATE. CROW and MEWE explore a compiler-based approach, whereas WASM-MUTATE employs a method based on binary. These tools automate the process of diversification, thereby increasing their practicality for deployment. At present, WASM-MUTATE is integrated into the wasmtime project! to improve testing. Our tools are complementary, providing combined utility. For instance, when the source code for a WebAssembly binary is unavailable, WASM-MUTATE offers an efficient solution for the generation of code variants. On the other hand, CROW and MEWE are particularly suited for scenarios that require a high level of variant preservation. Finally, one can use CROW and MEWE to generate a set of variants, which can then serve as rewriting rules for WASM-MUTATE. Moreover, when practitioners need to quickly generate variants, they could employ WASM-MUTATE, despite a potential decrease in the preservation of variants.

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lhttps://github.com/bytecodealliance/wasm-tools

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thesis

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Key results of the thesis.

Key results of the thesis

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Work

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Future Work.

Future Work

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Diversification

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Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of defensive software diversification to a WebAssembly standalone scenario.

Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of Defensive Software Diversification to a WebAssembly standalone scenario.

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Software

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Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of defensive software diversification to a WebAssembly standalone scenario.

Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of Defensive Software Diversification to a WebAssembly standalone scenario.

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Defensive

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Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of defensive software diversification to a WebAssembly standalone scenario.

Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of Defensive Software Diversification to a WebAssembly standalone scenario.

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programs

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Data augmentation for Machine Learning on WebAssembly programs.

Data augmentation for Machine Learning on WebAssembly programs

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In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief time span. Specifically, this entails producing millions of unique and diverse variants every second.

In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief timespan. Specifically, this entails producing millions of unique and diverse variants every second.

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timespan.

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In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief time span. Specifically, this entails producing millions of unique and diverse variants every second.

In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief timespan. Specifically, this entails producing millions of unique and diverse variants every second.

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Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, one-shot diversification usually happens during the software’s distribution or installation phase. The term “one shot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.

Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, oneshot diversification usually happens during the software’s distribution or installation phase. The term “oneshot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.

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“oneshot”

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Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, one-shot diversification usually happens during the software’s distribution or installation phase. The term “one shot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.

Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, oneshot diversification usually happens during the software’s distribution or installation phase. The term “oneshot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.

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oneshot

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Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, one-shot diversification usually happens during the software’s distribution or installation phase. The term “one shot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.

Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, oneshot diversification usually happens during the software’s distribution or installation phase. The term “oneshot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.

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Diversification

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Oneshot Diversification.

Oneshot Diversification

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insert '' by 'Object-Oriented'

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[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 - Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.

[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 -Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.

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-Object-Oriented

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[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 - Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.

[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 -Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.

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eople/Berners-Lee/WorldWideWeb.html,

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[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/People/Berners-Lee/WorldWideWeb.html, 1990.

[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/P eople/Berners-Lee/WorldWideWeb.html, 1990.

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https://www.w3.org/P

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[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/People/Berners-Lee/WorldWideWeb.html, 1990.

[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/P eople/Berners-Lee/WorldWideWeb.html, 1990.

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abs/1912.02285,

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[9] S. Narayan, T. Garfinkel, S. Lerner, H. Shacham, and D. Stefan, “Gobi: WebAssembly as a Practical Path to Library Sandboxing,” CoRR, vol. Abs/1912.02285, 2019.

[9] S. Narayan, T. Garfinkel, S. Lerner, H. Shacham, and D. Stefan, “Gobi: WebAssembly as a Practical Path to Library Sandboxing,” CoRR, vol. abs/1912.02285, 2019.

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“metaSafer:

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[19] S. Song, S. Park, and D. Kwon, “MetaSafer: A Technique to Detect Heap Metadata Corruption in WebAssembly,” IEEE Access, vol. 11, pp. 124887-— 124898, 2023.

[19] S. Song, S. Park, and D. Kwon, “metaSafer: A Technique to Detect Heap Metadata Corruption in WebAssembly,” IEEE Access, vol. 11, pp. 124887-— 124898, 2023.

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"

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[18] T. Rokicki, C. Maurice, M. Botvinnik, and Y. Oren, “Port Contention Goes Portable: Port Contention Side Channels in Web Browsers,” in ASIA CCS "22: ACM Asia Conference on Computer and Communications Security, pp. 1182-1194, 2022.

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[

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[17] M. N. Hoque and K. A. Harras, “WebAssembly for Edge Computing: Potential and Challenges,” [EEE Communications Standards Magazine, vol. 6, no. 4, pp. 68-73, 2022.

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php?title=National_Cyber_Leap_Year,

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[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.

[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.

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  • Year,

index.

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[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.

[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.

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gov/nitrdgroups/

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[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.

[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.

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https://www.nitrd.

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[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.

[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.

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gov

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[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.

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[

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[21 Q. Stiévenart, C. D. Roover, and M. Ghafari, “Security Risks of Porting C Programs to Webassembly,” in SAC ’22: The 87th ACM/SIGAPP Symposium on Applied Computing, pp. 1713-1722, 2022.

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“RAPID:

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[30] J. D. P. Rodriguez and J. Posegga, “RAPID Resource and API-based Detection Against In-browser Miners,” in Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018, pp. 313-326, ACM, 2018.

[30] J. D. P. Rodriguez and J. Posegga, “RAPID: Resource and API-based Detection Against In-browser Miners,” in Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018, pp. 313-326, ACM, 2018.

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insert '' by '28th'

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[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: Secure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security - 28th European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

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-28rd

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[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: Secure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security - 28th European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

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SEcure

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[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: Secure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security - 28th European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

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Hao

‘Hao’ is an imported foreign name or expression, which originally has a diacritic.

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[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.

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  • Hào

1{th

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[24] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “Thieves in the Browser: Web-based Cryptojacking in the Wild,” in Proceedings of the 1th International Conference on Availability, Reliability and Security, ARES 2019, Canterbury, UK, August 26-29, 2019, pp. 4:1-4:10, ACM, 2019.

[24] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “Thieves in the Browser: Web-based Cryptojacking in the Wild,” in Proceedings of the 1{th International Conference on Availability, Reliability and Security, ARES 2019, Canterbury, UK, August 26-29, 2019, pp. 4:1-4:10, ACM, 2019.

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{

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[24] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “Thieves in the Browser: Web-based Cryptojacking in the Wild,” in Proceedings of the 1{th International Conference on Availability, Reliability and Security, ARES 2019, Canterbury, UK, August 26-29, 2019, pp. 4:1-4:10, ACM, 2019.

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Speculative

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[23] G. Maisuradze and C. Rossow, “Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.

[23] G. Maisuradze and C. Rossow, “ret2spec: Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.

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“ret2spec:

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[23] G. Maisuradze and C. Rossow, “Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.

[23] G. Maisuradze and C. Rossow, “ret2spec: Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.

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Non-web

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[43] B. Spies and M. Mock, “An Evaluation of WebAssembly in Non-Web Environments,” in XLVII Latin American Computing Conference, CLEI 2021, Cartago, Costa Rica, October 25-29, 2021, pp. 1-10, IKEE, 2021.

[43] B. Spies and M. Mock, “An Evaluation of WebAssembly in Non-web Environments,” in XLVII Latin American Computing Conference, CLEI 2021, Cartago, Costa Rica, October 25-29, 2021, pp. 1-10, IKEE, 2021.

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  • Non-Web

lve?urn=urn:nbn:se:kth:diva-317331.

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[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317331.

[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/reso lve?urn=urn:nbn:se:kth:diva-317331.

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https://urn.kb.se/reso

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[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317331.

[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/reso lve?urn=urn:nbn:se:kth:diva-317331.

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Malivitsis,

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[36] J.C. Arteaga, O. F. Malivitis, O. L. V. Pérez, B. Baudry, and M. Monperrus, “Crow: Code diversification for webassembly,” in Proceedings of MadWEB, NDSS, 2021.

[36] J.C. Arteaga, O. F. Malivitsis, O. L. V. Pérez, B. Baudry, and M. Monperrus, “Crow: Code diversification for webassembly,” in Proceedings of MadWEB, NDSS, 2021.

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Inst

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[32] H. Okhravi, M. Rabe, T. Mayberry, W. Leonard, T. Hobson, D. Bigelow, and W. Streilein, “Survey of Cyber Moving Targets,” Massachusetts Institute of Technology Lexington Lincoln Lab, No. MIT/LL-TR-1166, 2013.

[32] H. Okhravi, M. Rabe, T. Mayberry, W. Leonard, T. Hobson, D. Bigelow, and W. Streilein, “Survey of Cyber Moving Targets,” Massachusetts Inst of Technology Lexington Lincoln Lab, No. MIT/LL-TR-1166, 2013.

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fiir

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[54] F. Marques, J. Fragoso Santos, N. Santos, and P. Adao, “Concolic Execution for WebAssembly,” Schloss Dagstuhl-Leibniz-Zentrum for Informatik, 2022.

[54] F. Marques, J. Fragoso Santos, N. Santos, and P. Adao, “Concolic Execution for WebAssembly,” Schloss Dagstuhl-Leibniz-Zentrum fiir Informatik, 2022.

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  • for

Comput.

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[53] T. Brito, P. Lopes, N. Santos, and J. F. Santos, “Wasmati: An efficient static vulnerability scanner for WebAssembly,” Computer. Secur., vol. 118, p. 102745, 2022.

[53] T. Brito, P. Lopes, N. Santos, and J. F. Santos, “Wasmati: An efficient static vulnerability scanner for WebAssembly,” Comput. Secur., vol. 118, p. 102745, 2022.

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  • Computer.

103,

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[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74—103, Springer, 2022.

[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm: Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74— 103, Springer, 2022.

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74—

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[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74—103, Springer, 2022.

[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm: Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74— 103, Springer, 2022.

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  • 74—103,

“SecWasm:

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[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74—103, Springer, 2022.

[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm: Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74— 103, Springer, 2022.

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  • “SecWasm

loT

replace 'loT' by 'LoT'

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[49] N. Makitalo, T. Mikkonen, C. Pautasso, V. Bankowski, P. Daubaris, R. Mikkola, and O. Beletski, “WebAssembly Modules as Lightweight Containers for Liquid LoT Applications,” in Proceedings of Web Engineering -21st International Conference, ICWE, vol. 12706, pp. 328-336, 2021.

[49] N. Makitalo, T. Mikkonen, C. Pautasso, V. Bankowski, P. Daubaris, R. Mikkola, and O. Beletski, “WebAssembly Modules as Lightweight Containers for Liquid loT Applications,” in Proceedings of Web Engineering -21st International Conference, ICWE, vol. 12706, pp. 328-336, 2021.

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  • LoT

arXiv:2305.13241,

replace 'arXiv:2305.13241,' by 'ArXiv:2305.13241,'

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[48] B. L. Titzer, “Whose baseline compiler is it anyway?,” arXiv e-prints, p. ArXiv:2305.13241, May 2023.

[48] B. L. Titzer, “Whose baseline compiler is it anyway?,” arXiv e-prints, p. arXiv:2305.13241, May 2023.

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  • ArXiv:2305.13241,

Applications,”

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[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, Understanding the Performance of Webassembly Applications, in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.

[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, “Understanding the Performance of Webassembly Applications,” in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.

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  • Applications,

“Understanding

replace '“Understanding' by 'Understanding'

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[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, Understanding the Performance of Webassembly Applications, in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.

[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, “Understanding the Performance of Webassembly Applications,” in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.

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  • Understanding

insert '' by 'transitions'

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[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near Zero-cost transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.

[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near-Zero-cost Transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.

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  • transitions

Transitions

replace 'Transitions' by 'Zero-cost'

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[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near Zero-cost transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.

[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near-Zero-cost Transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.

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  • Zero-cost

Near-Zero-cost

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[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near Zero-cost transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.

[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near-Zero-cost Transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.

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  • Near

abs/1802.01050,

replace 'abs/1802.01050,' by 'Abs/1802.01050,'

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[59] W. Fu, R. Lin, and D. Inge, “TaintAssembly: Taint-based Information Flow Control Tracking for WebAssembly,” CoRR, vol. Abs/1802.01050, 2018.

[59] W. Fu, R. Lin, and D. Inge, “TaintAssembly: Taint-based Information Flow Control Tracking for WebAssembly,” CoRR, vol. abs/1802.01050, 2018.

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  • Abs/1802.01050,

abs

This sentence does not start with an uppercase letter.

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[59] W. Fu, R. Lin, and D. Inge, “TaintAssembly: Taint-based Information Flow Control Tracking for WebAssembly,” CoRR, vol. abs/1802.01050, 2018.

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  • Abs

[

Unpaired symbol: ‘]’ seems to be missing

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[58] F. Breitfelder, T. Roth, L. Baumgartner, and M. Mezini, “WasmA: A Static WebAssembly Analysis Framework for Everyone,” in [EEE International Conference on Software Analysis, Evolution and Reengineering, SANER, pp. 753-757, 2023.

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“WaVe:

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[65] E. Johnson, E. Laufer, Z. Zhao, D. Gohman, S. Narayan, S. Savage, D. Stefan, and F. Brown, “Wave: A Verifiably Secure WebAssembly Sandboxing Runtime,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2028, pp. 2940-2955, IEEE, 2023.

[65] E. Johnson, E. Laufer, Z. Zhao, D. Gohman, S. Narayan, S. Savage, D. Stefan, and F. Brown, “WaVe: A Verifiably Secure WebAssembly Sandboxing Runtime,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2028, pp. 2940-2955, IEEE, 2023.

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  • “Wave:

Comput.

replace 'Comput.' by 'Computer.'

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[74] T. Thiim, S. Apel, C. Kastner, I. Schaefer, and G. Saake, “A classification and survey of analysis strategies for software product lines,” ACM Computer. Surv., vol. 47, jun 2014.

[74] T. Thiim, S. Apel, C. Kastner, I. Schaefer, and G. Saake, “A classification and survey of analysis strategies for software product lines,” ACM Comput. Surv., vol. 47, jun 2014.

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  • Computer.

ART

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[72] T. Y. Chen, F. Kuo, R. G. Merkel, and T. H. Tse, “Adaptive Random Testing: The Art of test case diversity,” J. Syst. Softw., vol. 83, no. 1, pp. 60-66, 2010.

[72] T. Y. Chen, F. Kuo, R. G. Merkel, and T. H. Tse, “Adaptive Random Testing: The ART of test case diversity,” J. Syst. Softw., vol. 83, no. 1, pp. 60-66, 2010.

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  • Art

Unpaired symbol: ‘‘’ seems to be missing

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[70] S. Sidiroglou-Douskos, S. Misailovic, H. Hoffmann, and M. C. Rinard, “Managing Performance vs. Accuracy Trade-offs With Loop Perforation,” in SIGSOFT/FSE’11 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-19) and ESEC’11: 13th European Software Engineering Conference (ESEC-13), pp. 124-134, 2011.

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insert '' by 'Foundations,'

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[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering - Foundations, Principles, and Techniques. Springer, 2005.

[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering -Foundations, Principles, and Techniques. Springer, 2005.

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  • Foundations,

-Foundations,

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[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering - Foundations, Principles, and Techniques. Springer, 2005.

[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering -Foundations, Principles, and Techniques. Springer, 2005.

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  • -

insert '' by 'Assembly'

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[66] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “New Kid on the Web: A Study on the Prevalence of Web Assembly in the Wild,” in Detection of Intrusions and Malware, and Vulnerability Assessment -16th International Conference, DIMVA, vol. 11543, pp. 23-42, 2019.

[66] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “New Kid on the Web: A Study on the Prevalence of WebAssembly in the Wild,” in Detection of Intrusions and Malware, and Vulnerability Assessment -16th International Conference, DIMVA, vol. 11543, pp. 23-42, 2019.

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  • Assembly

Diversity,”

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

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  • diversity,”

Software

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

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  • software

Dynamic

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

Suggestions

  • dynamic

Through

replace 'Through' by 'through'

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

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  • through

Attacks

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

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  • attacks

Side-channel

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

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  • side-channel

Cache

replace 'Cache' by 'cache'

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[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.

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  • cache

Unpaired symbol: ‘‘’ seems to be missing

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[85] K. Pettis and R. C. Hansen, “Profile Guided Code Positioning,” in Proceedings of the ACM SIGPLAN’90 Conference on Programming Language Design and Implementation (PLDI), pp. 16-27, 1990.

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abs/2308.03123,

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[96] S. Cao, N. He, Y. Guo, and H. Wang, “WASMixer: Binary Obfuscation for WebAssembly,” CoRR, vol. Abs/2308.03123, 2023.

[96] S. Cao, N. He, Y. Guo, and H. Wang, “WASMixer: Binary Obfuscation for WebAssembly,” CoRR, vol. abs/2308.03123, 2023.

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  • Abs/2308.03123,

abs

This sentence does not start with an uppercase letter.

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[96] S. Cao, N. He, Y. Guo, and H. Wang, “WASMixer: Binary Obfuscation for WebAssembly,” CoRR, vol. abs/2308.03123, 2023.

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  • Abs

Randomization,”

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[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

Suggestions

  • randomization,”

System

replace 'System' by 'system'

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[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

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  • system

Operating

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[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

Suggestions

  • operating

Overflows

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[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

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  • overflows

Buffer

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[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.

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  • buffer

Secur.

delete 'Secur.' by ''

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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Dependable

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • Security.

Trans.

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • Dependable

IEEE

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • Trans.

Layout,”

replace 'Layout,”' by 'IEEE'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • IEEE

Stack

replace 'Stack' by 'Layout,”'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • Layout,”

Randomizing

replace 'Randomizing' by 'Stack'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • Stack

by

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • Randomizing

Violations

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • by

Safety

replace 'Safety' by 'Violations'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • Violations

Memory

replace 'Memory' by 'Safety'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • Safety

Stack-based

replace 'Stack-based' by 'Memory'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • Memory

Thwarting

replace 'Thwarting' by 'Stack-based'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • Stack-based

“SaVioR:

replace '“SaVioR:' by 'Thwarting'

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[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • Thwarting

Kang,

replace 'Kang,' by '“SaVioR:'

Happy suggestion

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • “SaVioR:

B.

replace 'B.' by 'Kang,'

Happy suggestion

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

Suggestions

  • Kang,

insert '' by 'B.'

Happy suggestion

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.

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  • B.

The

delete 'The' by ''

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

1997.

replace '1997.' by 'yes.'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • yes.

Zealand,

replace 'Zealand,' by 'is'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • is

New

replace 'New' by 'answer'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • answer

Auckland,

replace 'Auckland,' by 'The'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • The

of

replace 'of' by '1997.'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • 1997.

University

replace 'University' by 'Zealand,'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Zealand,

Science,

replace 'Science,' by 'New'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • New

Computer

replace 'Computer' by 'Auckland,'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Auckland,

insert '' by 'University'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • University

Department

replace 'Department' by 'Science,'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Science,

rep.,

replace 'rep.,' by 'Computer'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Computer

tech.

replace 'tech.' by 'of'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • of

transformations,”

replace 'transformations,”' by 'Department'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Department

obfuscating

replace 'obfuscating' by 'tech.'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • tech.

of

replace 'of' by 'transformations,”'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • transformations,”

taxonomy

replace 'taxonomy' by 'obfuscating'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • obfuscating

“A

replace '“A' by 'of'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • of

Low,

replace 'Low,' by 'taxonomy'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • taxonomy

D.

replace 'D.' by '“A'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • “A

and

replace 'and' by 'Low,'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Low,

Thomborson,

replace 'Thomborson,' by 'D.'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • D.

C.

replace 'C.' by 'and'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • and

Collberg,

replace 'Collberg,' by 'Thomborson,'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Thomborson,

insert '' by 'Collberg,'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • Collberg,

insert '' by 'C.'

Happy suggestion

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.

[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The

Suggestions

  • C.

abs/2305.14591,

replace 'abs/2305.14591,' by 'Abs/2305.14591,'

Happy suggestion

[106] K. Zhang, D. Wang, J. Xia, W. Y. Wang, and L. Li, “ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,” CoRR, vol. Abs/2305.14591, 2023.

[106] K. Zhang, D. Wang, J. Xia, W. Y. Wang, and L. Li, “ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,” CoRR, vol. abs/2305.14591, 2023.

Suggestions

  • Abs/2305.14591,

abs

This sentence does not start with an uppercase letter.

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[106] K. Zhang, D. Wang, J. Xia, W. Y. Wang, and L. Li, “ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,” CoRR, vol. abs/2305.14591, 2023.

Suggestions

  • Abs

sep

replace 'sep' by 'Sep'

Happy suggestion

[103] E. Schulte, Z. P. Fry, E. Fast, W. Weimer, and S. Forrest, “Software mutational robustness,” vol. 15, p. 281-312, Sep 2014.

[103] E. Schulte, Z. P. Fry, E. Fast, W. Weimer, and S. Forrest, “Software mutational robustness,” vol. 15, p. 281-312, sep 2014.

Suggestions

  • Sep

(101]

replace '(101]' by '(101)'

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(101) B. R. Churchill, O. Padon, R. Sharma, and A. Aiken, “Semantic Program Alignment for Equivalence Checking,” in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, pp. 1027-1040, 2019.

(101] B. R. Churchill, O. Padon, R. Sharma, and A. Aiken, “Semantic Program Alignment for Equivalence Checking,” in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, pp. 1027-1040, 2019.

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  • (101)

(

Unpaired symbol: ‘)’ seems to be missing

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(101] B. R. Churchill, O. Padon, R. Sharma, and A. Aiken, “Semantic Program Alignment for Equivalence Checking,” in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, pp. 1027-1040, 2019.

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(

Unpaired symbol: ‘)’ seems to be missing

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(109] P. M. Phothilimthana, A. Thakur, R. Bodfk, and D. Dhurjati, “Scaling up Superoptimization,” in Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, pp. 297-310, 2016.

Suggestions

Integr.

replace 'Integr.' by 'Integrity.'

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[116] G. Agosta, A. Barenghi, G. Pelosi, and M. Scandale, “The MEET Approach: Securing Cryptographic Embedded Software Against Side Channel Attacks,” IEEE Trans. Comput. Aided Des. Integrity. Circuits Syst., vol. 34, no. 8, pp. 1820-1333, 2015.

[116] G. Agosta, A. Barenghi, G. Pelosi, and M. Scandale, “The MEET Approach: Securing Cryptographic Embedded Software Against Side Channel Attacks,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., vol. 34, no. 8, pp. 1820-1333, 2015.

Suggestions

  • Integrity.

rep.,

delete 'rep.,' by ''

Happy suggestion

[114] B. Salamat, A. Gal, T. Jackson, K. Manivannan, G. Wagner, and M. Franz, “Stopping Buffer Overflow Attacks at Run-Time: Simultaneous Multi-variant Program Execution on a Multicore Processor,” tech. Technical Report 07-13, School of Information and Computer Sciences, UCIrvine, 2007.

[114] B. Salamat, A. Gal, T. Jackson, K. Manivannan, G. Wagner, and M. Franz, “Stopping Buffer Overflow Attacks at Run-Time: Simultaneous Multi-variant Program Execution on a Multicore Processor,” tech. rep., Technical Report 07-13, School of Information and Computer Sciences, UCIrvine, 2007.

Suggestions

441,

delete '441,' by ''

Happy suggestion

[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process Replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434—441, IEEE Computer Society, 2007.

[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434— 441, IEEE Computer Society, 2007.

Suggestions

434—

replace '434—' by '434—441,'

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[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process Replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434—441, IEEE Computer Society, 2007.

[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434— 441, IEEE Computer Society, 2007.

Suggestions

  • 434—441,

replicee

replace 'replicee' by 'Replicee'

Happy suggestion

[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process Replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434—441, IEEE Computer Society, 2007.

[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434— 441, IEEE Computer Society, 2007.

Suggestions

  • Replicee

27:13,

delete '27:13,' by ''

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

27:1—

delete '27:1—' by ''

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

pp.

replace 'pp.' by '27:1—27:13,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • 27:1—27:13,

2022,

replace '2022,' by 'pp.'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • pp.

10-14,

replace '10-14,' by '2022,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • 2022,

October

replace 'October' by '10-14,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • 10-14,

USA,

replace 'USA,' by 'October'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • October

MI,

replace 'MI,' by 'USA,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • USA,

Rochester,

replace 'Rochester,' by 'MI,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • MI,

2022,

replace '2022,' by 'Rochester,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Rochester,

ASE

replace 'ASE' by '2022,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • 2022,

Engineering,

replace 'Engineering,' by 'ASE'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • ASE

Software

replace 'Software' by 'Engineering,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Engineering,

Automated

replace 'Automated' by 'Software'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Software

on

replace 'on' by 'Automated'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Automated

Conference

replace 'Conference' by 'on'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • on

International

replace 'International' by 'Conference'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Conference

IEEE/ACM

replace 'IEEE/ACM' by 'International'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • International

87th

replace '87th' by 'IEEE/ACM'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • IEEE/ACM

in

replace 'in' by '87th'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • 87th

Binaries,”

replace 'Binaries,”' by 'in'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • in

of

replace 'of' by 'Binaries,”'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Binaries,”

Generation

replace 'Generation' by 'of'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • of

Guided

replace 'Guided' by 'Generation'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Generation

Feedback

replace 'Feedback' by 'Guided'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Guided

for

replace 'for' by 'Feedback'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Feedback

Framework

replace 'Framework' by 'for'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • for

A

replace 'A' by 'Framework'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Framework

:

replace ':' by 'A'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • A

“Cornucopia

replace '“Cornucopia' by ':'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • :

Machiry,

replace 'Machiry,' by '“Cornucopia'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • “Cornucopia

A.

replace 'A.' by 'Machiry,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Machiry,

and

replace 'and' by 'A.'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • A.

Kulkarni,

replace 'Kulkarni,' by 'and'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • and

M.

replace 'M.' by 'Kulkarni,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Kulkarni,

Saumya,

replace 'Saumya,' by 'M.'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • M.

C.

replace 'C.' by 'Saumya,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Saumya,

Pillai,

replace 'Pillai,' by 'C.'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • C.

A.

replace 'A.' by 'Pillai,'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • Pillai,

insert '' by 'A.'

Happy suggestion

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.

[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.

Suggestions

  • A.

abs/2110.03301,

replace 'abs/2110.03301,' by 'Abs/2110.03301,'

Happy suggestion

[124] H. Bostani and V. Moonsamy, “EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection,” CoRR, vol. Abs/2110.03301, 2021.

[124] H. Bostani and V. Moonsamy, “EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection,” CoRR, vol. abs/2110.03301, 2021.

Suggestions

  • Abs/2110.03301,

abs

This sentence does not start with an uppercase letter.

Happy suggestion

[124] H. Bostani and V. Moonsamy, “EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection,” CoRR, vol. abs/2110.03301, 2021.

Suggestions

  • Abs

abs/2111.11487,

replace 'abs/2111.11487,' by 'Abs/2111.11487,'

Happy suggestion

[135] P. Dasgupta and Z. Osman, “A Comparison of State-of-the-art Techniques for Generating Adversarial Malware Binaries,” CoRR, vol. Abs/2111.11487, 2021.

[135] P. Dasgupta and Z. Osman, “A Comparison of State-of-the-art Techniques for Generating Adversarial Malware Binaries,” CoRR, vol. abs/2111.11487, 2021.

Suggestions

  • Abs/2111.11487,

abs

This sentence does not start with an uppercase letter.

Happy suggestion

[135] P. Dasgupta and Z. Osman, “A Comparison of State-of-the-art Techniques for Generating Adversarial Malware Binaries,” CoRR, vol. abs/2111.11487, 2021.

Suggestions

  • Abs

How

delete 'How' by ''

Happy suggestion

[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.

[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “ARMED: How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.

Suggestions

“ARMED:

replace '“ARMED:' by '“How'

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[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.

[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “ARMED: How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.

Suggestions

  • “How

insert '' by 'March'

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[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 8, 2023, The Internet Society, 2023.

[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 -March 8, 2023, The Internet Society, 2023.

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-March

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[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 8, 2023, The Internet Society, 2023.

[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 -March 8, 2023, The Internet Society, 2023.

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  • -

March 8, 2023

A new year has begun. Did you mean “March 8, 2024”?

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[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 -March 8, 2023, The Internet Society, 2023.

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“babble:

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[145] D. Cao, R. Kunkel, C. Nandi, M. Willsey, Z. Tatlock, and N. Polikarpova, “Babble: Learning Better Abstractions with E-Graphs and Anti-unification,” Proc. ACM Program. Lang., vol. 7, no. POPL, pp. 396-424, 2023.

[145] D. Cao, R. Kunkel, C. Nandi, M. Willsey, Z. Tatlock, and N. Polikarpova, “babble: Learning Better Abstractions with E-Graphs and Anti-unification,” Proc. ACM Program. Lang., vol. 7, no. POPL, pp. 396-424, 2023.

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CoRR,

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[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. Abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR.

[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR,

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  • CoRR.

abs/1711.04422,

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[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. Abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR.

[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR,

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abs

This sentence does not start with an uppercase letter.

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[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR,

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Comput.

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[150] F. Cohen, “Computer Viruses: Theory and Experiments,” Computer. Secur., vol. 6, no. 1, pp. 22-35, 1987.

[150] F. Cohen, “Computer Viruses: Theory and Experiments,” Comput. Secur., vol. 6, no. 1, pp. 22-35, 1987.

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Transformations,”

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[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via semantic-preserving code transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.

[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.

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Code

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[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via semantic-preserving code transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.

[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.

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Semantic-preserving

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[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via semantic-preserving code transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.

[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.

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Diagnostics,”

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[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

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  • diagnostics,”

Execution

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[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

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Test

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[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

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Repair

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[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

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  • repair

Program

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[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.

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arXiv:2305.19915,

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[155] T. Y. Zhuo, Z. Yang, Z. Sun, Y. Wang, L. Li, X. Du, Z. Xing, and D. Lo, “Source Code Data Augmentation for Deep Learning: A Survey,” arXiv e-prints, p. ArXiv:2305.19915, May 2023.

[155] T. Y. Zhuo, Z. Yang, Z. Sun, Y. Wang, L. Li, X. Du, Z. Xing, and D. Lo, “Source Code Data Augmentation for Deep Learning: A Survey,” arXiv e-prints, p. arXiv:2305.19915, May 2023.

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  • ArXiv:2305.19915,